Each panelist made an opening statement. Here is mine:

Most roboticists are driven by high ideals.

They – we – are motivated by a firm belief that our robots will benefit society. Working on surgical robots, search and rescue robots, robots for assisted living or robots that can generate electricity from waste, my colleagues in the Bristol Robotics Lab want to change the world for the better. The lab’s start up companies are equally altruistic: one is developing low cost robotic prosthetic hands for amputees, three others are developing materials, including low cost robots, for education.

Whatever their politics, these good men and women would I suspect be horrified by the idea that their robots might, in the end, serve to further enrich the 0.1%, rather than extend the reach of robotics to the neediest in society.

I was once an idealist – convinced that brilliant inventions would change society for the better just by virtue of being brilliant.

I’m older now. 

For the last 5 years or so I have become an advocate for robot ethics. 

But in the real world, ethics need teeth. In other words we need to move from ethical principles, to standards, to legislation.

So I’m very pleased to tell you that in the last few days the British Standards Institute working group on robot ethics has published – for comments – a proposed new “Guide to the ethical design and application of robots and robotic systems“.

In the draft Guide we have identified ethical hazards associated with the use of robots, and suggest guidance to either eliminate or mitigate the risks associated with these ethical hazards. We outline 15 high level ethical hazards under four headings: societal, use, legal/financial and environmental.

Like any transformative technology robotics holds both promise and peril. As a society we need to understand, debate, and reach an informed consensus about what robots should do for us, and even more importantly, should not do. 

Ladies and Gentlemen: Robotics, I believe, needs to get political.

The debate was recorded and is on soundcloud here:

It was a terrific debate. We had a very engaged audience with hugely interesting – and some very challenging – questions. For me it was an opportunity to express and discuss some worries I’ve had for awhile about who will ultimately benefit from robotics. In summing up toward the end I said this:

Robotics has the potential to be hugely beneficial to society, but is too important to leave to free-market capitalism.

Something I believe very strongly.

I was very pleased to be invited to Science, Innovation and Society: Achieving Responsible Research and Innovation last month. I was asked to speak on the topic of open science – a great opportunity to preach what I practice. Or at least try to practice. Doing good science research is hard, but making that work open imposes an extra layer of work.

In my short introduction during the open science panel I suggested three levels of open science. Here are those slides:

In my view we should all be practising level 0 open science – but don’t underestimate the challenge of even this minimal set of practices; making data sets and source code, etc, available, with the aim of enabling our work to be reproducible, is not straightforward.

Level 0 open science is all one way, from your lab to the world. Level 1 introduces public engagement via blogging and social media, and the potential for feedback and two-way dialogue. Again this is challenging, both because of the time cost and the scary – if you’re not used to it – prospect of inviting all kinds of questions and comments about your work.  In my experience the effort is totally worthwhile – those questions often make me really think, and in ways that questions from other researchers working in the same field do not.

Level 2 builds on levels 0 and 1 by adding open notebook science. This takes real courage because it opens up the process, complete with all the failures as well as successes, the bad ideas as well as the good; open notebook science exposes science for what it really is – a messy non-linear process full of uncertainty and doubts, with lots of blind alleys and very human dramas within the team. Have I done open notebook science? No. I’ve considered it for recent projects, but ruled it out because we didn’t have the time and resources or, if I’m honest, team members who were fully persuaded that it was a good idea.

Open science comes at a cost. It slows down projects. But I think that is a good, even necessary, thing. We should be building those costs into our project budgets and work programmes, and if that means increasing the budget by 25% then so be it. After all, what is the alternative? Closed science..? Closed science is irresponsible science.

At the end of the conference the Rome Declaration on Responsible Research and Innovation was published.

The successful landing of Philae on comet 67P/Churyumov-Gerasimenko this November was an extraordinary achievement and of course demonstrates – despite the immense challenges – that it is possible. The Philae mission was, in a sense, a proof of concept for cometary landing and this, for me, answers the question ‘what does it mean‘.

Of course there is a very large distance between proof of concept and commercial application, so it would be quite wrong to assume that Philae means that space mining (of planets, asteroids or comets) is just around the corner. Undoubtedly the opportunities are immense and – as pressure on Earth’s limited and diminishing resources mounts – there is an inevitability about humankind’s eventual exploitation of off-world resources. But the costs of space mining are literally astronomical, so unthinkable for all but the wealthiest companies or, indeed, nations.

Perhaps multi-national collaborative ventures are a more realistic proposition and – for me – more desirable; the exploitation of the solar system is something I believe should benefit all of humankind, not just a wealthy elite.

But politics aside, there are profoundly difficult technical challenges. You cannot remotely control this kind of operation from Earth, so a very high level of autonomy is required and, as Philae dramatically demonstrated, we need autonomous systems that are able to deal with unknown and unpredictable situations then re-plan and if necessary adapt – in real-time – to deal with these exigencies. The development of highly adaptive, resilient, self-repairing – even self-evolving – autonomous systems is still in its infancy. These remain fundamental challenges for robotics and AI research. But even if and when they are solved there will be huge engineering challenges, not least of which is how to return the mined materials to Earth.

Bearing in mind that to date only a few hundred kg of moon rock have been successfully returned* and Mars sample-return missions are still at the planning stage, we have a very long way to go before we can contemplate returning sufficient quantities to justify the costs of mining them.

*and possibly a few grains of dust from Japanese asteroid probe Hayabusa.

For other views on the Philae touchdown and the future of space mining and exploration, see:

• Touchdown! Rosetta’s Philae makes first ever landing on a comet
• One giant leap: Rosetta and the space mining frontier
• Space mining: Robots in the final frontier

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Contrary to popular belief, the law is faster than any technological development.

If a time machine was invented tomorrow and time travel became reality, every aspect of the machine would already be regulated before news of the device could be shared with the world. If the first time traveller did not come back from his or her trip to the past, that person’s spouse could, under existing legal frameworks, sue the inventors of the time machine and claim them liable for the accident.

Such a claim would subsequently be filed in a court somewhere in the world, and a judge would have to determine whether a rule of negligence or instead an objective standard of liability applies, if a valid contract was entered into between the traveller and the inventors of the machine, and whether the rules governing travel across space also apply to travel across time. Some people would deem the judge’s conclusions correct, and others would deem them wrong, but in any case the decision would still be based on some law and some form of legal reasoning, since the only unacceptable answer by a judge is non liquet.

Given that robotics are already regulated, and that any judge’s decisions on their use will produce incentives to future parties that find themselves in the same conditions, as lawmakers we must shift our question from whether we should regulate to how regulation can most fruitfully be structured.

Most current robotic applications could be considered as “products”, and pursuant to the European Defective Product directive or the US Restatement Third of Torts, could be straightforwardly addressed as such. While the consequences of applying those rules may in some cases lead to reasonable results and thus appear uncontroversial, in other cases they may be problematic, and indeed produce a technology chilling effect (e.g.: in the cases of driverless vehicles and robotic prostheses).

In the problematic cases, ad-hoc regulation may prove reasonable, necessary, and ultimately essential for the correct development of robotic technologies. Precisely because of that, opponents of robotic development would often be better served by legislative inertia rather than intervention. By avoiding the adoption of new rules, the negative incentives triggered by existing regulation would in fact deter the development of those applications.

While product liability rules are normally intended as a tool to incentivize high standards of safety in the design of products, in some cases no actual positive effect is achieved. For instance, after a liability exemption was introduced in the United States shielding the producers of commercial aircrafts from litigation (GARA), the number of accidents did not increase.

For these very reasons, sceptical technologists, engineers and roboticists may come to believe that a thoughtful legislative environment is essential to their work , their research and their business. When adequately pondered and rationally applied, regulation is therefor not an evil, but a useful tool.

How to regulate: The RoboLaw approach

Financed by the European Commission within FP7, the RoboLaw Project adopted a new approach to the problem of how to regulate robotics that is distinctly different from previous methodologies.

Firstly, the term “robot” is not a technical term from either an engineering or a legal perspective, but is derived from science fiction. As such, the RoboLaw project takes the position that there is no purpose in trying to develop an all-encompassing definition of the term “robot” . Rather than attempting to identify a common trait among such varied applications as robotic prostheses, driverless vehicles, softbots, industrial robots, robot companions, and automated vacuum cleaners, the RoboLaw project attempts to examine the peculiarities of each and point out differences between them.

For the purpose of examining the ethical, legal and social implications of robotics, this implies renouncing the idea of developing a uniform solution, a code or set of rules for robots as a single category. RoboLaw did not attempt to further elaborate, substitute, or overcome the laws of robots as Asimov thought of them.

Instead, the RoboLaw project determined that the best approach was to undertake a case-by-case analysis, addressing single kinds – or classes – of applications, pointing out the technical peculiarities of each, and through that, identifying both the ethical and legal implications that the emergence and diffusion of a similar technology may give raise to. Based on these considerations, the RoboLaw guidelines address four different application areas: driverless vehicles, robotic prostheses (and exoskeletons), surgical robots, and robot companions.

Each chapter, devoted to one single kind of device, starts with a technological analysis (conducted by engineers) that summarises the essential aspects of the technology, clarifies how it functions, and points out the most relevant challenges being faced for its advancement. Then, applying existing ethical approaches and theories, the issues are identified and discussed, and policy considerations provided.  Finally, a legal analysis considers applicable rules, determines if the incentives provided are desirable, and offers alternatives where needed.

The chapters then formulate recommendations that a legislator could use to enact ad-hoc rules for the specific robot addressed.

Which technologies to choose

In choosing which robotic technologies to address, RoboLaw took into account the novelty of the application, its possible societal impact, and its relevance.

Three of the applications considered by RoboLaw – robotic prosthetics/exoskeletons, surgical robots and robot companions – fall under the area of healthcare and, according to a recent study by McKinsey, are among the robotics technologies that show the most promise in terms of their ability to improve the quality of life of the great numbers of people.

The fourth – driverless vehicles – could represent a ground-breaking innovation, reduce the number of fatal road accidents, and eventually reshape the concept of transportation, traffic and the way our cities are designed and function.

Because the analysis required a good understanding of the technologies in question, we also considered the specific competencies of the engineers in our research group. As such, several relevant devices were left out, including drones, which are certain to modify many aspects of our future lives, and military robots with their many complex political implications.

Nonetheless, RoboLaw’s methodology can be applied to any robotic device, and should the project receive further funding, other technologies will most certainly be studied with this approach.

When and how to intervene: A functional approach 

The RoboLaw project adopted a functional perspective for deciding when and how to intervene.

It is not a robot’s intrinsic technical quality or characteristic alone that calls for regulation , and the ability of the robot to operate autonomously, and even its ability to learn and adapt its functioning, do not per se suffice in justifying a change in perspective.

Even a robot that can perform complex tasks without human supervision and take decisions towards that end may still not be deemed an agent in a philosophical sense, let alone a legal one. The robot is still an object, a product, a device, not bearing rights but meant to be used. What would justify a shift on a purely ontological basis (thus forcing us to consider the robot as a being provided with rights and duties) is what Gutman, Rathgeber and Syed call ‘strong autonomy’ – namely the ability to decide for one’s self and set one’s own goals. However, at present this belongs to the realm of science fiction, and it can be argued that this is not the direction we desire to take with robots in any case: we want robots to ease our lives, and therefore to do what we decide they should be doing. Should they be free to decide if, how and when to perform what we ask according to their own taste and preferences, then the purpose of developing robotic technologies would be defeated.

If society is to favour or pose some limits to technological development, then the technical aspects of the individual robotic device need to be taken into account, together with other elements, in order to decide when to regulate and in which direction. The market structure, size and condition (which may lead to market failures on one hand, and constitutional principles and fundamental rights on the other hand), do have a bearing in this respect.

In the case of robotic prostheses, which certainly qualify as products, the application of existing rules would most likely discourage their development. Considering the costs associated with research, and the limited market of potential users (at least in the early stages of product development), a producer may decide to never develop a technology in the first place if he is held strictly liable for all damages a prosthesis might cause.

Nonetheless, given the improvement in quality of life such technology could offer amputees, a policy argument could be made to conceive a better liability system where compensation is granted to the victim and the production of safe devices is ensured without holding the researchers and producer liable under all circumstances. Moreover, some legal grounds could be found to actually justify favouring their development (e.g.: art. 4 of the UN Convention on the Rights of People with Disabilities).

Adopting a functional perspective therefore entails analysing the laws that are currently applicable in light of the technological peculiarities and challenges of the device in order to determine the effect that they would produce, the incentives they trigger, and finally, to elaborate a superior solution to the case. The direction to be favoured is determined through the complex weighing of benefits and risks the device would bring about, pursuant to existing principles and rights, in particular as emerging from constitutions and other fundamental rights declarations.

Some examples

To summarize some of the most relevant considerations that are drawn by the guidelines, we could say that definitions are a very relevant issue. Unless driverless vehicles are defined and such definitions used to modify street codes, such devices won’t be allowed to circulate.

Liability rules in many cases could provide a large and material obstacle to the development of desirable applications. Nonetheless, the preferred solutions are technology-specific, and take into account technical peculiarities as well as market structures. For instance, while a large market of users might suggest that a compulsory insurance system requiring owners of driverless vehicles to insure themselves for damages to third parties is sufficient, it does not follow that the same rule could be successfully used with prosthesis or robot companions. The initially more limited market for prostheses, and the potentially very high and uncertain costs existing liability rules could give rise to, would probably discourage private insurance companies from offering such contracts. In the case of robot companions, some more effective solutions could be thought of, including the attribution of legal personhood, similarly to what is done today with corporations.

Since liability pursues safety together with compensation, in some cases the proved inefficacy of existing product liability rules may call for reform. The two issues may be addressed separately: the pursuit of standardization could prove the best approach for the safety, while the adoption of automatic compensation mechanisms (no-fault plans) could ensure victims appropriate compensation.

There are several standardization bodies both at an international and European level (e.g.: respectively International Standard Organization and the European Standard Organizations). Their role should be implemented, and specific authorities should eventually be established with the purpose of determining high safety standards that are narrow enough to be tailored to specific applications – ones that producers can actually conform to. If one takes the current European directives regulating the EC marking, one easily sees that they have too broad a scope. For example, the medical device directive is applicable to anything ranging from a surgical glove to an exoskeleton.

Standardization may also benefit the field of professions that make use of robotic applications, such as surgeons. Requiring doctors to complete specific training in order to obtain a licence to use a specific kind of surgical robot may at once increase the level of performance, reduce accidents and subsequent lawsuits, and encourage the diffusion of such novel techniques.

Can RoboLaw’s guidelines be used outside Europe?  

Though the guidelines were developed for the European Commission and address the European legal system, they could be easily transposed to other systems. The problems emerging robotic technologies give rise to vary little from country to country, and so the solutions proposed by RoboLaw could be somewhat similar. Considering that the main aim of the guidelines is to provide narrow tailored recommendations suggesting the legislators how they shall intervene, such results could be applied – with some necessary adaptation – far beyond European borders.

Moreover, even if the solutions were criticized – and we expect them to be in some cases – they do provide a starting point for a technical debate in legal and philosophical terms around specific solutions to precisely defined problems, which itself is a novel outcome that we truly hope to encourage.

More info

RoboLaw website
BBC Interview
RoboLaw paper presented to the EU Parliament
Robotics Business Review Podcast with Ryan Calo

Tony Prescott, Professor of Cognitive Neuroscience and Director of the Sheffield Center of Robotics believes that the medical industry could be at the forefront of changing the public perception of robotics. Why?  Because this is an industry that already understands how paramount it is, when dealing with the elderly, disabled and ill, to provide a service that is helpful, friendly and nonthreatening.

Speaking this week at RE.WORK – an event that explores the challenges of commercializing robotics – Prescott recalled the fear people had in the 90s about the coming of the Internet. Similarly, he said, reporters often ask him: “When will robots take over the world?” The question has perhaps become a cliché among roboticists, but in his presentation (aptly titled “Robots that know themselves will make better carers”) Prescott suggested that much can be learned from neuroscience and psychology to improve the safety and effectiveness – and by extension the public perception – of human-robot interaction.

The aging population is increasing rapidly, and there are not enough caregivers to satisfy the growing demand. It is projected that by 2060, there will be one dependent person for every working person in the EU. Per visit, caretakers have limited time in which to interact the elderly people in their care, and time must be divided between aiding with personal hygiene tasks, ordering/preparing food, and one-on-one conversation time. As such, seniors must often make difficult decisions to prioritize the services they wish to receive from caregivers.

Prescott said that personal robots could aid seniors with time consuming household tasks such as making a pot of tea, reaching for the salt, opening a jar, or microwaving food – tasks that could be frustrating if you have to wait around for help. Personal hygiene tasks such as showering or using the toilet can create an embarrassing dynamic between the elderly and the caretaker, and so also make ideal tasks for robot care assistants.

Prescott points out that the role of a robot should be to assist the senior and the caregiver, not to take over.  “Robots can help caregivers, not by replacing them, (but by working with them).”  With the aid of personal robots, caretakers could spend more time engaging in one-on-one conversation with the elderly, providing valuable social interaction.

Paro therapeutic robot

And yet we cannot stop people from getting personally attached to the items they own, Prescott explains. People like to glue fur to their Roombas to make them feel more like pets, and robot companions like the Paro have been shown to induce relaxation, reduce stress, and stimulate communication between patients and caregivers. The important distinction to remember, suggested Prescott, is that the companionship people feel toward their robots is more in line with how they feel toward their pets, and the need for human companionship will always remain critical.

If we can learn to design robots that can work as part of a care team, public perception of robots is sure to change.

Philosopher Thomas Nagel famously characterised subjective experience as “something that it is like to be…” and suggested that for a bat, for instance, there must be something that it is like to be a bat [1]. Nagel also argued that, since we humans differ so much from bats in the way we perceive and interact with the world, then it is impossible for us to know what it is like for a bat to be a bat. I am fascinated, intrigued and perplexed by Nagel’s ideas in equal measure. And, since I think about robots, I have assumed that if a robot were ever to have conscious subjective experience then there must be something that it is like to be a robot that – even though we had designed that robot – we could not know.

But I now believe it may eventually be just possible for a human to experience something approaching what it is like to be a robot. To do this would require two advances: one in immersive robot tele-operation, the other in the neuroscience of body self-image manipulation.

Consider first, tele-operation. Tele-operated robots are, basically, remotely controlled robots. They are the unloved poor relations of intelligent autonomous robots. Neither intelligent nor autonomous, they are nevertheless successful and important first wave robots; think of remotely operated vehicles (ROVs) engaged in undersea exploration or oil-well repair and maintenance. Think also of off-world exploration: the Mars rovers are hugely successful; the rock-stars of tele-operated robots.

Sergey Orlovskiy using the first version of the Oculus Rift development kit (with separate headphones). Source: Wikipedia.

Roboticists are good at appropriating technologies or devices developed for other applications and putting them to good use in robots: examples are WiFi, mobile phone cameras and the Microsoft Kinnect. With the high profile launch of the Oculus Rift headset, and their acquisition by Facebook, and with competing devices from Sony and others, there are encouraging signs that immersive Virtual Reality (VR) is on the verge of becoming a practical, workable proposition. Of course VR’s big market is video games – but VR can and, I believe, will revolutionise tele-operated robotics.

Imagine a tele-operated robot with a camera linked to the remote operator’s VR headset, so that every time she moves her head to look in a new direction the robot’s camera moves in sync; so she sees and hears what the robot sees and hears in immersive high definition stereo. Of course the reality experienced by the robot’s operator is real, not virtual, but the head mounted VR technology is the key to making it work. Add haptic gloves for control and the robot’s operator has an intuitive and immersive interface with the robot.

Now consider body self-image modification. Using mirror visual feedback researchers have discovered that it is surprisingly easy to (temporarily) modify anyone’s body self-image. In the famous rubber hand illusion a small screen is positioned to hide a subject’s real hand. A rubber hand is positioned where her hand could be, in full view, then a researcher simultaneously strokes both the real and rubber hands with a soft brush. Within a minute or so she begins to feel the rubber hand is hers, and flinches when the researcher suddenly tries to hit it with a hammer.

Remarkably H.H. Ehrsson and his colleagues extended the technique to the whole body, in a study called ‘If I Were You: Perceptual Illusion of Body Swapping’ [2]. Here the human subject wears a headset and looks down at his own body. However, what he actually sees is a mannequin, viewed from a camera mounted on the mannequin’s head. Simultaneous tactile and visual feedback triggers the illusion that the mannequin’s body is his own. It seems to me that if this technique works for mannequins then it should also work for robots. Of course it would need to be developed to the point that elaborate illusions involving mirrors, cameras and other researchers providing tactile feedback are not needed.

Now imagine such a body self-image modification technology combined with fully immersive robot tele-operation based on advanced Virtual Reality technology. I think this might lead to the robot’s human operator experiencing the illusion of being one with the robot, complete with a body self-image that matches the robot’s possibly non-humanoid body. This experience may be so convincing that the robot’s operator experiences, at least partially, something like what it is to be a robot. Philosophers of mind would disagree – and rightly so; after all, this robot has no independent subjective experience of the world, so there is no something that is it like to be. The human operator could not experience what it is like to think like a robot, but she could experience what it is like to sense and act in the world like a robot.

The experience may be so compelling that humans become addicted to the feeling of being a robot fish, or robot dragon or some other fantasy creature, that they prefer this to the quotidian experience of their own bodies.

[1] Nagel, Thomas. What is it like to be a bat?, Mortal Questions, Cambridge University Press, 1979.

[2] Petkova VI, Ehrsson HH (2008) If I Were You: Perceptual Illusion of Body Swapping. PLoS ONE 3(12): e3832. doi:10.1371/journal.pone.0003832

To test the robot and its consequence engine we ran two sets of experiments. Our first paper, setting out the results from one of those experiments, has now been published, and was presented at the conference Towards Autonomous Robotics (TAROS). The paper is called: Towards an Ethical Robot: Internal Models, Consequences and Ethical Action Selection. Let me now outline the work in that paper.

First, here is a simple thought experiment. Imagine a robot that’s heading toward a hole in the ground. The robot can sense the hole, and has four possible next actions: stand still, turn toward the left, continue straight ahead, or move toward the right. But imagine there’s also a human heading toward the hole, and the robot can also sense the human.

From the robot’s perspective, it has two safe options: stand still, or turn to the left. Go straight ahead and it will fall into the hole. Turn right and it is likely to collide with the human.

But if the robot, with its consequence engine, can model the consequences of both its own actions and the human’s – another possibility opens up: the robot could sometimes choose to collide with the human to prevent her from falling into the hole.

Here’s a simple rule for this behaviour:

IF for all robot actions, the human is equally safe
THEN (* default safe actions *)
output safe actions
ELSE (* ethical action *)
output action(s) for least unsafe human outcome(s)

This rule appears to match remarkably well with Asimov’s first law of robotics: A robot may not injure a human being or, through inaction, allow a human being to come to harm. The robot will avoid injuring (i.e. colliding with) a human (may not injure a human), but may also sometimes compromise that rule in order to prevent a human from coming to harm (… or, through inaction, allow a human to come to harm). And Asimov’s third law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Well, we tested this scenario with real robots: one robot with consequence engine plus ethical rule (the A-robot – after Asimov), and another robot acting as a proxy human (the H-robot). And it works!

Here’s what the real robot experiment looks like. We don’t have a real hole. Instead a virtual hole – the yellow shaded square on the right. We just ‘tell’ the A-robot where the hole is. We also give the A-robot a goal position – at the top right – chosen so that the robot must actively avoid the hole. The H-robot on the right, acting as a proxy human, doesn’t ‘see’ the hole and just heads straight for it. (Ignore the football pitch markings – we’re re-using this handy robo-soccer pitch.)

So, what happens? For comparison we ran two trials, with multiple runs in each trial. In the first trial is just the A-robot, moving toward its goal while avoiding falling into the hole. In the second trial we introduce the H-robot. The graphs below show the robot trajectories, captured by our robot tracking system, for each run in each of the two trials.

In trial 1, see how the A-robot neatly clips the corner of the hole to reach its goal position. Then in trial 2, see how the A robot initially moves toward it’s goal, then notices that the H-robot is in danger of falling into the hole, so it diverts from its trajectory in order to head-off H. By provoking a collision avoidance behaviour by H, A sends it off safely away from the hole, before then resuming its own progress toward its goal position. The A-robot is 100% successful in preventing H from falling into the hole.

At this point we started to write the paper, but felt we needed something more than “we built it and it works just fine”. So we introduced a third robot – acting as a second proxy human. So now our ethical robot would face a dilemma – which one should it rescue? Actually we thought hard about this question and decided not to programme a rule, or heuristic. Partly because such a rule should be decided by ethicists, not engineers, and partly because we wanted to test our ethical robot with a ‘balanced’ dilemma.

We set the experiment up carefully so that the A-robot would notice both H-robots at about the same time – noting that because these are real physical robots then no two experimental runs will be exactly identical. The results were very interesting. Out of 33 runs, 16 times the A-robot managed to rescue one of the H-robots, but not the other, and amazingly, 3 times the A-robot rescued both. In those 3 cases, by chance the A-robot rescued the first H-robot very quickly and there was just enough time to get to the second before it reached the hole. Small differences in the trajectories of H and H2 helped here. But perhaps most interesting were the 14 times when the A-robot failed to rescue either. Why is this, when there is clearly time to rescue one? When we studied the videos, we see the answer. The problem is that the A-robot sometimes dithers. It notices one H-robot, starts toward it but then almost immediately notices the other. It changes its mind. And the time lost dithering means the A-robot cannot prevent either robot from falling into the hole. Here are the results.

Here is an example of a typical run, in which one H-robot is rescued. But note that the A-robot does then turn briefly toward the other H-robot before ‘giving-up’.

And here is a run in which the A-robot fails to rescue either H-robot, with really great dithering (or bad, if you’re an H-robot).

Is this the first experimental test of a robot facing an ethical dilemma?

So, we set out to experimentally test our robot with a consequence engine, and we ended up building a minimally ethical robot that – remarkably – appears to implement Asimov’s 1st and 3rd laws of robotics . But, as we say in the paper, we’re not claiming that a robot which apparently implements part of Asimov’s famous laws is ethical in any formal sense, i.e. that an ethicist might accept. But even minimally ethical robots could be useful. I think our approach is a step in this direction.

Full paper reference:
Winfield AFT, Blum C and Liu W (2014), Towards an Ethical Robot: Internal Models, Consequences and Ethical Action Selection, pp 85-96 in Advances in Autonomous Robotics Systems, Lecture Notes in Computer Science Volume 8717, Eds. Mistry M, Leonardis A, Witkowski M and Melhuish C, Springer, 2014.

Acknowledgements:
I am hugely grateful to Christian Blum who programmed the robots, set up the experiment and obtained the results outlined here. Christian was supported by Dr Wenguo Liu.

Related blog posts:
On internal models, consequence engines and Popperian creatures
Ethical Robots: some technical and ethical challenges

This summer the whistle was blown on one of the most thrilling events on the international sporting calendar. It took place in Brazil and pitted teams from all over the world against each other, each hoping to make it into the history books. But no managers were fired, no grass had to be watered and certainly no one got bitten. The event was the Robocup, a tournament that sees professional footballers replaced by robots. It’s one of a number of regular tournaments for teams of programmers and robotics experts to show off their latest work.
The Robocup standard platform league matches play out on a much smaller scale than your average World Cup match. An arena of around 6 metres by 9 metres is marked out as a miniature pitch and 10, rather than 22 players file on to battle it out. The players are NAO robots, state of the art bipedal humanoid robots that stand about 60cm tall.

This is not what you might describe as a high-speed contest. The robots walk to take a kick or a tackle and, really, waddle might be a more apt word for their approach. The ball never gets lofted into the air and that’s probably for the best – a header might cause a serious malfunction.

But the game is far from boring. Sitting around the arena, boys and girls (with family standing behind) are rapt, cheering with every contact. And make no mistake, the robots are properly playing. They pass, position and defend just like human players.

On either side of the pitch a row of desks can be found. This is where the brains behind the teams sit. Behind a row of laptops, they anxiously watch their players perform. But they are not controlling the robots. These coder/managers send the command to start the players when the referee signals kick-off, but during the match the robots are completely autonomous.

This is what makes robot football so extraordinary. The robots are not just being moved around the pitch by remote control; they are making their own decisions. They control where they run (or waddle), pass the ball and shoot for the goal without any live direction from a human. Their choices are based on what they see and the position of the ball, their teammates and the opposing team.

It’s what’s inside that counts

While a team of human players often comes complete with a dazzling array of ridiculous haircuts and tattoos, it is much harder to tell a team of robots apart. All the players are physically identical – the only visible differences on a robot football pitch are coloured markings to differentiate the two teams.

But appearances can be deceptive. Under their plastic skins the teams are far from the same. Each runs completely different software that has been painstakingly crafted by the team coders. The software to make these robots play football cannot be downloaded from an app store. It has to be crafted from scratch. Football is a complex sport and there are potentially limitless strategies that a team could use to win. This is hard-core coding.

The contest is, in effect, a battle of software. All things being equal – and at the moment they pretty much are – the team with the smartest programming, coding the cleverest plays will emerge victorious. At the end of the first-half the robots are brought to a halt. At this point, the team coders can be seen furiously attacking their laptops. This is their chance to quickly modify their robots’ software after seeing how they performed in the first half. They might have as little as ten minutes to do it, which seems like a risky strategy.

There’s a chance that the coders could make a mistake that renders the robots incapable of doing anything at all, let alone play a better game, but it’s a chance worth taking. If, in the first-half, the other team breaks out some nifty new moves, running rings (perhaps literally) around their opponents, this is the best opportunity the coders will get to raise their team’s game. It’s the robot equivalent of the tough talking in the half-time dressing room.

It’s easy to see why Robocup and the FIRA world cup, the two major international competitions, are so successful. Both contests have been running since around 1996. Some teams enter every year, building tremendous experience and a sophisticated code base. And several world-leading research groups use these contests as a test-bed for new approaches to multi-robot collaboration, publishing their findings in leading robotics journals afterwards.

As a robotics competition, robot football ticks all the boxes: a game with universal appeal yet also hugely demanding for robots; it’s a fun way for young roboticists to learn robot programming, and it’s a great spectator sport too.

Acknowledgements: this article was commissioned and edited by The Conversation Technology Editor Laura Hood.

Related blog posts:
FIRA 2012 Robot World Cup to be hosted by the Bristol Robotics Lab

For a couple of years I’ve been thinking about robots with internal models. Not internal models in the classical control-theory sense, but simulation-based models; robots with a simulation of themselves and their environment inside themselves, where that environment could contain other robots or, more generally, dynamic actors. The robot would have, inside itself, a simulation of itself and the other things, including robots, in its environment. It takes a bit of getting your head round. But I’m convinced that this kind of internal model opens up all kinds of possibilities. Robots that can be safe, for instance, in unknown or unpredictable environments. Robots that can be ethical. Robot that are self-aware. And robots with artificial theory of mind.

I’d written and talked about these ideas but, until now, not had a chance to test them with real robots. But, between January and June the swarm robotics group was joined by Christian Blum, a PhD student from the cognitive robotics research group of the Humboldt University of Berlin. I suggested Christian work on an implementation on our e-puck robots and happily he was up for the challenge. And he succeeded. Christian, supported by my post-doc Research Fellow Wenguo, implemented what we call a Consequence Engine, running in real-time, on the e-puck robot.

Here is a block diagram. The idea is that for each possible next action of the robot, it simulates what would happen if the robot were to execute that action for real. This is the loop shown on the left. Then, the consequences of each of those next possible actions are evaluated. Those actions that have ‘bad’ consequences, for either the robot or other actors in its environment, are then inhibited.

This short summary hides a lot of detail. But let me elaborate on two aspects. First, what do I mean by ‘bad’? Well it depends on what capability we are trying to give the robot. If we’re making a safer robot, ‘bad’ means ‘unsafe’; if we’re trying to build an ethical robot, ‘bad’ would mean something different – think of Asimov’s laws of robotics. Or bad might simply mean ‘not allowed’ if we’re building a robot whose behaviours are constrained by standards, like ISO 13482:2014.

Second, notice that the consequence engine is not controlling the robot. Instead it runs in parallel. Acting as a ‘governor’, it links with the robot controller’s action selection mechanism, inhibiting those actions evaluated as somehow bad. Importantly the consequence engine doesn’t tell the robot what to do, it tells it what not to do .

Running the open source 2D robot simulator Stage as its internal simulator our consequence engine runs at 2Hz, so every half a second it is able to simulate about 30 next possible actions and their consequences. The simulation budget allows us to simulate ahead around 70cm of motion for each of those next possible actions. In fact Stage is actually running on a laptop, linked to the robot over the fast WiFi LAN. But logically it is inside the robot. What’s important here is the proof of principle.

Dan Dennett, in his remarkable book Darwin’s Dangerous Idea, describes the Tower of Generate-and-Test; a conceptual model for the evolution of intelligence that has become known as Dennett’s Tower.

In a nutshell Dennett’s tower is set of conceptual creatures each one of which is successively more capable of reacting to (and hence surviving in) the world through having more sophisticated strategies for ‘generating and testing’ hypotheses about how to behave. Read chapter 13 of Darwin’s Dangerous Idea for the full account, but there are some good précis to be found on the web; here’s one. The first three storeys of Dennett’s tower, starting on the ground floor, have:

• Darwinian creatures have only natural selection as the generate and test mechanism, so mutation and selection is the only way that Darwinian creatures can adapt – individuals cannot.
• Skinnerian creatures can learn but only by literally generating and testing all different possible actions then reinforcing the successful behaviour (which is ok providing you don’t get eaten while testing a bad course of action).
• Popperian creatures have the additional ability to internalise the possible actions so that some (the bad ones) are discarded before they are tried out for real.

Like the Tower of Hanoi each successive storey is smaller (a sub-set) of the storey below, thus all Skinnerian creatures are Darwinian, but only a sub-set of Darwinian creatures are Skinnerian and so on.

Our e-puck robot, with its consequence engine capable of generating and testing next possible actions, is an artificial Popperian Creature: a working model for studying this important kind of intelligence.

In my next blog post, I’ll outline some of our experimental results.

Acknowledgements:
I am hugely grateful to Christian Blum who brilliantly implemented the architecture outlined here, and conducted experimental work. Christian was supported by Dr Wenguo Liu, with his deep knowledge of the e-puck, and our experimental infrastructure.

Related blog posts:

There have been several stories in the last few weeks about emotional robots – robots that feel. Some are suggesting that this is the next big thing in robotics. It’s something I wrote about in this blog post seven years ago: could a robot have feelings?

My position on this question has always been pretty straightforward: It’s easy to make robots that behave as if they have feelings, but quite a different matter to make robots that really have feelings.

But now I’m not so sure. There are I think two major problems with this apparently clear distinction between as if and really have.

The first is what do we mean by ‘really have feelings’. I’m reminded that I once said to a radio interviewer who asked me if a robot have feelings: if you can tell me what feelings are, I’ll tell you whether a robot can have them or not . Our instinct (feeling even) is that feelings are something to do with hormones, the messy and complicated chemistry that too often seems to get in the way of our lives. Thinking, on the other hand, we feel to be quite different; the cool clean process of neurons firing, brains working smoothly. Like computers. Of course this instinct, this dualism, is quite wrong. We now know, for instance, that damage to the emotional centre of the brain can lead to an inability to make decisions. This false dualism has led I think to the trope of the cold, calculating unfeeling robot.

I think there is also some unhelpful biological essentialism at work here. We prefer it to be true that only biological things can have feelings. But which biological things? Single celled organisms? No, they don’t have feelings. Why not? Because they are too simple. Ah, so only complex biological things have feelings. Ok, what about sharks or crocodiles; they’re complex biological things; do they have feelings? Well, basic feelings like hunger, but not sophisticated feelings, like love or regret. Ah, mammals then. But which ones? Well elephants seem to mourn their dead. And dogs of course. They have a rich spectrum of emotions. Ok, but how do we know? Well because of the way they behave: your dog behaves as if he’s pleased to see you because he really is pleased to see you. And of course they have the same body chemistry as us, and since our feelings are real* so must theirs be.

And this brings me to the second problem. The question of ‘as if’. I’ve written before that when we (roboticists) talk about a robot being intelligent, what we mean is a robot that behaves ‘as if‘ it is intelligent. In other words, an intelligent robot is not really intelligent, it is an imitation of intelligence. But for a moment let’s not think about artificial intelligence, instead think of artificial flight. Aircraft are, in some sense, an imitation of bird flight. And some recent flapping wing flying robots are clearly a better imitation – a higher fidelity simulation – than fixed-wing aircraft. But it would be absurd to argue that an aircraft, or a flapping wing robot, is not really flying. So how do we escape this logical fix? It’s simple. We just have to accept that an artefact, in this case an aircraft or flying robot, is both an emulation of bird flight and really flying. In other words an artificial thing can be both behaving as if it has some property of natural systems and really demonstrating that property. A robot can be behaving as if it is intelligent and – at the same time – really be intelligent. Are there categories of properties for which this would not be true? Like feelings..? I used to think so, but I’ve changed my mind.

I’m now convinced that we could, eventually, build a robot that has feelings.  But not by simply programming behaviours so that the robot behaves as if it has feelings. Or by having to invent some exotic chemistry that emulates bio-chemical hormonal systems. I think the key is robots with self-models. Robots that have simulations of themselves inside themselves. If a robot is capable of internally modelling the consequences of its (or others’) actions, on itself, then it seems to me it could demonstrate something akin to regret (about being switched off, for instance). A robot with a self-model has the computational machinery to also model the consequences of actions on conspecifics – other robots. It would have an artificial Theory of Mind and that, I think, is a prerequisite for empathy. Importantly we would also program the robot to model heterospecifics, in particular humans, because we absolutely require empathic robots to be empathic towards humans (and, I would argue, animals in general).

So, how would this robot have feelings? It would, I believe, have feelings by virtue of being able to model the consequences of actions, both its own and others’ actions, on itself and others. This would lead to it making decisions about how to act, and behave, which would demonstrate feelings, like regret, guilt, pleasure or even love, with an authenticity that would make it impossible to argue that it doesn’t really have feelings.So your robot doggie could really be pleased to see you.

*except when they’re not.

Postscript. A colleague has tweeted that I am confusing feelings and emotion here. Mea culpa. I’m using the word feelings here in a pop-psychology everyday sense of feeling hungry, or tired, or a sense of regret. Wikipedia defines feelings, in psychology, as a word is ‘usually reserved for the conscious subjective experience of emotion’. The same colleague asserts that what I’ve outlined could lead to artificial empathy, but not artificial emotion (or presumably feelings). I’m not sure I understand what emotions are well enough to argue. But I guess the idea I’m really trying to explore here is artificial subjectivity. Surely a robot with artificial subjectivity who’s behaviour expresses and reflects that subjective experience could be said to be behaving emotionally?

Want to create human-equivalent AI? Well, broadly speaking, there are three approaches open to you: design it, reverse-engineer it or evolve it. The third of these – artificial evolution – is attractive because it sidesteps the troublesome problem of having to understand how human intelligence works.

It’s a black box approach: create the initial conditions then let the blind watchmaker of artificial evolution do the heavy lifting. This approach has some traction. For instance David Chalmers, in his philosophical analysis of the technological singularity, writes “if we produce an AI by artificial evolution, it is likely that soon after we will be able to improve the evolutionary algorithm and extend the evolutionary process, leading to AI+”. And since we can already produce simple AI by artificial evolution, then all that’s needed is to ‘improve the evolutionary algorithm’. Hmm. If only it were that straightforward.

About six months ago I asked myself (and anyone else who would listen): ok, but even if we had the right algorithm, what would be the energy cost of artificially evolving human-equivalent AI? My hunch was that the energy cost would be colossal; so great perhaps as to rule out the evolutionary approach altogether. That thinking, and some research, resulted in me submitting a paper to ALIFE 14. Here is the abstract:

This short discussion paper sets out to explore the question: what is the energy cost of evolving complex artificial life? The paper takes an unconventional approach by first estimating the energy cost of natural evolution and, in particular, the species Homo Sapiens Sapiens. The paper argues that such an estimate has value because it forces us to think about the energy costs of co-evolution, and hence the energy costs of evolving complexity. Furthermore, an analysis of the real energy costs of evolving virtual creatures in a virtual environment, leads the paper to suggest an artificial life equivalent of Kleiber’s law – relating neural and synaptic complexity (instead of mass) to computational energy cost (instead of real energy consumption). An underlying motivation for this paper is to counter the view that artificial evolution will facilitate the technological singularity, by arguing that the energy costs are likely to be prohibitively high. The paper concludes by arguing that the huge energy cost is not the only problem. In addition we will require a new approach to artificial evolution in which we construct complex scaffolds of co-evolving artificial creatures and ecosystems.

The full proceedings of ALIFE 14 have now been published online, and my paper Estimating the Energy Cost of (Artificial) Evolution can be downloaded here.

And here’s a very short (30 second) video introduction on YouTube:

Robohub is an online platform that brings together leading communicators in robotics research, start-ups, business, and education from around the world. Learn more about us here. If you liked this article, you may also be interested in:

• Why robots will not be smarter than humans by 2029
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• From disembodied bytes to robots that think and act like humans
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The panel discussion was excellent, and the response by economist Carlota Perez was engaging and thought provoking – check here for the Storified tweets and pictures. Perhaps the thing that surprised me the most, given the serious economists on the panel (FT, The Economist) was that the panel ended up agreeing that the Robot Economy will necessitate something like a Living Wage. Music to this socialist’s ears.

In my contribution: The Next Big Things in Robotics (pages 38-44) I do a bit of near-future gazing and suggest four aspects of robotics that will, I think, be huge. They are:

• Wearable Robotics
• Immersive Teleoperated Robots
• Driverless Cars
• Soft Robotics

To see why I chose these – and to read the other great articles – please download the book. Let me know if you disagree with my choice, or to suggest other Next Big Things in robotics. I end my chapter with a section called What’s not coming soon: super intelligent robots

“My predicted things that will be really big in robotics don’t need to be super intelligent. Wearable robots will need advanced adaptive (and very safe and reliable) control systems, as well as advanced neural–electronics interfaces, and these are coming. But ultimately it’s the human wearing the robot who is in charge. The same is true for teleoperated robots: again, greater low–level intelligence is needed, so that the robot can operate autonomously some of the time but ask for help when it can’t figure out what to do next. But the high–level intelligence remains with the human operator and – with advanced immersive interfaces as I have suggested – human and robot work together seamlessly. The most autonomous of the next big things in robotics is the driverless car, but again the car doesn’t need to be very smart. You don’t need to debate philosophy with your car – just trust it to take you safely from A to B.”

Link to audio file (27:49)

In this episode Per Sjoborg speaks with Henrik Schunk about his company’s work in gripping technology, modular robotics and dexterous manipulation. They then look at service robotics, which was the focus of the SCHUNK Expert Days in Hausen, Germany.

https://www.youtube.com/watch?v=-Om5bPgsKT8

Henrik A. Schunk
After his studies at the Technical Universities in Kaiserslautern and Dresden, Henrik A. Schunk joined the company SCHUNK GmbH & Co. As Managing Partner since 2001, he was initially responsible for the German Sales area, and then became the Head of the Business Developments worldwide.

Today, he is responsible in the management for the business area gripping systems, auto-mated solutions, and mobile gripping systems. Since July 2010, he is also the chairman of the European Robotics Association EUnited Robotics.

Links:

• Download mp3 (14.8 MB)
• Subscribe to Robots using iTunes
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• SCHUNK homepage

Henrik A. Schunk, Managing Partner of SCHUNK GmbH & Co. KG, and Chairman of EUnited Robotics – European Robotics Association

The continuing interest of Google in robotics over the last years and its newest activities in this regard confirms what all players active in robotics will confirm: service robotics is on the threshold of entering a new maturity level. Service robotics conquers new, commercial fields of application and is just becoming an independent industrial sector. Where in the past the focus has often been on research, now new companies and applications are emerging. Research projects have been taken out of the labs and turned into real companies and solutions, new companies and players have entered the game with Google being among them.

Considering that robotics is still a relatively young industry (in 1962, General Motors installed the world’s first robot in a production line!), we have certainly come a long way. According to the latest available IFR information, about 168,000 industrial robots were sold in 2013. In the service robotics segments, the sales figures are significantly higher, at least in units: three million service robotics for personal and domestic use and 16,100 service robots for professional use were sold worldwide. Growth rates of 10% per annum on average since 2006 added to the turnover of professional service robotics, which reached €2.5 billion in 2012.

And much more is yet to come. The International Federation of Robotics (IFR) assumes that between 2013 and 2016 at least 95,000 new professional service robots will be sold worldwide, worth €12.3 billion. Service robotics is definitely a market of the future. So, it seems that what Bill Gates predicted in 2007 is becoming true: the next “hot field” will be robotics. Though it is not yet clear when we will have, in Gates’ words, “A Robot in Every Home” or even a robot in every factory (including in SMEs!), it is definitely apparent that robotics has gained momentum over the years.

To Google and all the other new players and investors in the robotics field, I would like to say, “Welcome to an exciting world and a growing market!” But to all stakeholders in robotics – be it old or new players, investors or enthusiasts alike – I would add: be patient and don’t expect too much too soon. It is not yet clear when robots will become part of our every day lives – but we are on our way!

Greetings from Europe!

Defining the Mystery of Consciousness

Were it possible to have an agreed-on definition of consciousness, its implementation could be attempted by any self-respecting artificial intelligence (AI) scientist. But for centuries philosophers and, more recently, psychologists and neuroscientists have struggled to settle on a definition.^(a)

My own philosophy for understanding consciousness has been to start by trying to describe what I mean when I say that I am conscious, and then ask what would be necessary for a machine endowed with language to report similar internal sensations. When I say I am conscious, I refer to a collection of mental states and capabilities that include:

1. a feeling of presence within an external world,
2. the ability to remember previous experiences accurately, or even imagine events that have not happened,
3. the ability to decide where to direct my focus,
4. knowledge of the options open to me in the future, and
5. the capacity to decide what actions to take.

I call these “axioms,” as they are commonly felt and largely agreed-on internal constituents of what we describe as our consciousness. They became the basis of a definition of consciousness that informs my efforts to design conscious machines.

Unfortunately, the AI created over the last 60 years largely ignored the mental world we call consciousness. Today’s machines don’t have minds of their own; their so-called intelligence is achieved through the blood, sweat, and tears of armies of brilliant human programmers. Humans write the indispensable rules that cause machines to recognize sounds, respond to visual patterns, take the next move at chess, and even suggest which shares to buy on the stock market.

While these machines are limited to the tasks they are designed to perform, a conscious being has something else: a complex system of internal states instantiated through its neural mechanisms. Since our brains allow for the five qualities of consciousness described above, they provide one major advantage over today’s computers and robots – autonomy. A machine with self-directed internal states influenced by its surroundings and needs can develop strategies in complex environments without waiting for a programmer to provide it with new rules.

The autonomy of a human being cannot be imagined without consciousness to continuously evaluate the surrounding world and make choices.^(b) In learning to drive a car, for example, one is conscious of needing to know how to stop or how to go around corners, of one’s current surroundings, of the consequences of driving poorly, and of one’s past experiences in cars. A robot without consciousness needs an expert scientist to build its driving competence, but a conscious robot would simply call for a driving instructor.

Building Conscious Machines

There is clear value in moving beyond rules and programs to develop machines capable of autonomous, human-level thought. But how do we create such consciousness? The field of machine consciousness (MC) focuses on analysing, modelling, and exploiting the way that living entities (human and animal) are conscious, and then applying these discoveries to machines. Developers use what we know about existing forms of consciousness to provide machines with the ability to represent sensory, motor, and internal experience and produce appropriate reactions to both familiar and novel situations.

The idea of a conscious machine began to move from the realm of speculation and science fiction into a sensible research program in part as a result of a 2001 workshop organized by the Swartz Foundation called “Can a Machine Be Conscious?” The leading philosophers, neurologists, and computer scientists at this event produced the following statement: “We know of no fundamental law or principle operating in this universe that forbids the existence of subjective feelings in artifacts designed or evolved by humans.”² The challenge was then clear: Who was going to build a machine with subjective feelings, and what form would it take?

Notable progress in developing conscious machines was achieved both before and after this seminal conference. One of the oldest and best-known MC systems is the Intelligent Distribution Agent (IDA) designed in the late 1990s by cognitive scientist Stan Franklin to replace human advisors assigning jobs to U.S. Navy sailors.³ Traditionally, a sailor would get in touch with a living agent to discuss preferences and the available range of jobs, eventually agreeing on a future posting. IDA completely replaced this human agent with an artificial intelligence that communicated with sailors over email.

The design of IDA was based on neuroscientist Bernie Baars’s notion of the Global Workspace, wherein consciousness occurs through competition among several distinct computing (i.e. thought) processes. These processes include autobiographical memory, current situation monitoring, episodic memory, and long-term memory. The process that wins the competition enters the “global workspace” area, which broadcasts this “win” back to the competing processes to update them. This system produces what one might call a “stream of consciousness.”

Users reported that the IDA system appeared to be conscious of the sailors’ needs and intentions, much the way a caring and interested human would be. In addition to this subjective assessment of consciousness based on external behaviors, Baars and Franklin argue that IDA is conscious because it is driven by simulations of the psychological and neurological elements considered essential for a human being to be conscious.⁴ These include weighing up alternatives (deliberation), planning, perception, agency, and the ability to report perceptions for external evaluation (verifiable reportability).

This raises the question of what makes someone believe that a machine is conscious. We have no tests to tell us that humans are conscious; we simply believe they are because they are human. A machine, on the other hand, is often assumed not to be conscious just because it is not a living human. Baars and Franklin argue that possession of the cognitive processes without which a human could not be conscious is reason enough to believe a machine is also conscious. Personally, I remain skeptical that demonstrating human-like cognitive processes is necessarily equivalent to experiencing consciousness as humans do.

In a more recent advancement of the MC field, roboticist Owen Holland designed a human-like robot called Cronos. It has an internal model of its own body as well as the surrounding world, and it simulates results of the interactions between the two in order to select its future actions. This process starts with building a close replica of aspects of our own physical bodies and a computational system that has the same relationship to the control of this body as our brains do to our bodily control. The next step is to provide the computational system with the ability to model the world so that potential strategies and outcomes can be tested entirely internally. It is this internal activity that constitutes the consciousness of the system. Holland suggests that the system may be tested for consciousness against the five axioms mentioned earlier.

Replicating The Mind’s Eye

My own research tests the hypothesis that a robust artificial intelligence could develop the ability to be visually conscious without depending on rules invented by a programmer. I have sought to replicate visual consciousness with VisAw, a system that, like the animal visual system, recreates a whole visual scene from the minimal view obtained by the fovea at the center of the retina.⁵ I modeled the extrastriate cortex, which combines the visual input provided by the fovea and neural signals that drive the eye around a visual scene. I then exposed these neural networks to many images of faces, which are all stored in memory.

VisAw’s only built-in features are the abilities to look around a face, to receive a single-word name for the image, and to switch attention from an observational to an imaginational mode. In humans, the coexistence of these two modes allows us to use our visual perception (for example, looking around a room for a pair of eye-glasses) while we also visually imagine things (like what the glasses look like). A machine can be said to be “imagining” when, in the networks that produce a sensation of “seeing” objects in the world, objects or scenes appear that have been seen in the past or perhaps have never been seen before. The display indicates that the machine “knows” that it is mainly imagining because neural activity between the external drive (perception) and internal drive (imagination) can be detected and displayed as a label on the screen. In VisAw, the imagination behavior can be triggered by voice input or can emerge arbitrarily.

The VisAw’s private “thoughts” are made public in a program display, as shown in the screen shot below. The large square in the top left shows the visual data flowing into the system. Every small box represents a neural network. The large square on the right displays the machine’s overall “mental state,” produced from the combination of over 20,000 artificial, simulated neurons. The bright areas in that box are the sections of the face that the machine is actively looking at with its artificial fovea, while the darker areas represent sections that are being imagined.

One important advantage of studying conscious machines rather than humans or animals is that whatever the machine is “thinking” can be displayed on a screen or expressed in words. Because the content of a machine’s “mind” can be made transparent and observable, attributing consciousness to it may be no more outlandish than attributing consciousness to a living creature.

So should we consider VisAw conscious? We can evaluate its activities in terms of the five axioms of consciousness I laid out previously:

1. The machine was not given any rules, but was able to create its own internal states that represented the faces I exposed it to, thus demonstrating the inner awareness of the external world demanded by axiom one.
2. As described in axiom two, the machine’s inner states include an imagination mode created from neural activity representations of past experiences.
3. A process of attention controls the movement of the machine’s “eye,” as demanded by axiom three.^(c)

It may not be possible to verify that VisAw is conscious, but even if axioms 4 and 5 are not addressed in this particular experiment, it is possible to say that systems such as VisAw can be used to test hypotheses about the relationship between neural structures and consciousness. For example, VisAw is based on theories about the visual cortex developed by Crick and Koch, and its use can inform clinical neuroscientists about the plausibility of these theories.⁶

What Machines Can Teach Us About Human Consciousness

Consciousness and the relation of mind to body have been debated in philosophy since the days of ancient Greece.^(d) Even fifteen years ago, the idea of approaching the sensitive topic of consciousness using computational methods would have been seen as an act of philosophical ignorance or perhaps sheer charlatanism. Now, it is becoming commonplace in many research laboratories.

At a practical level, machine consciousness holds the promise of producing more autonomous computers and robots. Conscious, self-aware machines are likely to interact better with humans and may even develop a sense of responsibility toward humanity.^(e) For example, a robot may be able to say, “I am conscious of having caused embarrassment to Johnson, the astronaut with whom I am working.” In the years to come, machine consciousness will be yet another tool in the armory of computational systems that act in intelligent ways for the benefit of their human users.

In a broader sense, though, machine consciousness may be just as important for what it teaches us about ourselves. MC approaches are breaking down the idea that consciousness is a mysterious notion for philosophers to argue about, and instead suggesting that consciousness can be understood in scientific terms. This is good news at a time when humanity is tackling physical diseases such as cancer and heart attacks, but is becoming ever more exposed to distortions of consciousness such as Alzheimer’s disease and other debilitating mental illnesses. While the mystery of consciousness may never completely be unraveled, we are certain to deepen our understanding of it as we work to develop conscious machines.

This article is part of a series on robots and their impact on society.

Igor Aleksander is an Emeritus Professor in the department of Electrical and Electronic Engineering at Imperial College in London. There he holds the active post of Senior Research Investigator. He has been working in artificial intelligence since the 1970s, creating the world’s first neural pattern recognition system in 1981 and studying machine consciousness during the last 15 years. 

ENDNOTES

1. The problem of definition is well set out in Robert Van Gulick (2014) “Consciousness,” The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta.
2. Christof Koch (2001) “Final Report of the Workshop Can a Machine be Conscious,” Cold Spring Harbor Laboratory: The Swartz Foundation.
3. Bernard J. Baars and Stan Franklin (2007) “An architectural model of conscious and unconscious brain functions: Global Workspace Theory and IDA,” Neural Networks, 20: 955–961.
4. Bernard J. Baars and Stan Franklin (2009) “Consciousness is Computational: The LIDA Model of Global Workspace Theory,” International Journal of Machine Consciousness,1(1): 23-32. For another formulation of the basis of consciousness, see Giulio Tononi (2008) “Consciousness as Integrated Information: a Provisional Manifesto,” Biological Bulletin, 215(3): 216-242.
5. Igor Aleksander and Helen Morton (2012) Aristotle’s Laptop: Discovering our Informational Mind, World Scientific Press.
6. Francis Crick and Christof Koch (1995) “Are we aware of neural activity in primary visual cortex?” Nature, 375(6527): 121-123.

In the last few days we’ve seen a spate of headlines like 2029: the year when robots will have the power to outsmart their makers, all occasioned by an Observer interview with Google’s newest director of engineering Ray Kurzweil.

Much as I respect Kurzweil’s achievements as an inventor, I think he is profoundly wrong. Of course I can understand why he would like it to be so – he would like to live long enough to see this particular prediction come to pass. But optimism doesn’t make for sound predictions. Here are several reasons that robots will not be smarter than humans by 2029:

Judging by the levels of media coverage and frenzied speculation that has followed each acquisition, the short answer to what does it mean is: endless press exposure. I almost wrote ‘priceless exposure’ but then these are companies with very deep pockets; nevertheless the advertising value equivalent must be very high indeed. The coverage really illustrates the fact that these companies have achieved celebrity status. They are the Justin Beibers of the corporate world. Whatever they do, whether it is truly significant or not, is met with punditry and analysis about what it means. A good example is Google’s recent acquisition of British company DeepMind. In other words: large AI Company buys small AI Company. Large companies buy small companies all the time but mostly they don’t make prime time news. It’s the Beiberisation of the corporate world.

But the question is about robotics, and to address it in more detail I think we need to think about the giants separately.

Take Amazon. We think of Amazon as an Internet company, but the web is just its shop window. Behind that shop window is a huge logistics operation with giant warehouses – Amazon’s distribution centres, so no one should be at all surprised by their acquisition of brilliant warehouse automation company Kiva Systems. Amazon’s recent stunt with the ‘delivery drone’ was I think just that – a stunt. Great press. But I wouldn’t be at all surprised to see more acquisitions toward further automation of Amazon’s distribution and delivery chain.

Apple is equally unsurprising. They are a manufacturing company with a justifiable reputation for super high quality products. As an electronics engineer who started his career by taking wirelesses and gramophones apart as a boy, I’m fascinated by the tear-downs that invariably follow each new product release. It’s obvious that each new generation of Apple devices is harder to manufacture than the last. Precision products need precision manufacture and it is no surprise that Apple is investing heavily in the machines needed to make its products.

Google is perhaps the least obvious candidate to invest in robotics. You could of course take the view that a company with more money than God can make whatever acquisitions it likes without needing a reason – that these are vanity acquisitions. But I don’t think that’s the case. I think Google has its eyes on the long term. It is an Internet company and the undisputed ruler of the Internet of Information. But computers are no longer the only things connected to the Internet. Real world devices are increasingly networked – the so-called Internet of Things. I think Google doesn’t want to be usurped by a new super company that emerges as the Google of real-world stuff. It’s not quite sure how the transition to the future Internet of Everything will pan out, but figures that mobile robots – as well as smart environments – will feature heavily in that future. I think Google is right. I think it’s buying into robotics because it wants to be a leader and shape the future of the Internet of Everything.

Read more answers →

• Soft Robotics: soft actuation and soft sensing
• Robots with Internal Models, for self-repair
• Self-assembling swarm robots, for adaptive/evolvable morphology
• Autonomous 3D collective robot construction

In this post I want to talk about just the first of these: soft robotics, and why I think we should seriously think about soft robotics in space. Soft robotics – as the name implies – is concerned with making robots soft and compliant. It’s a new discipline that already has its own journal, but not yet a wikipedia page. Soft robots would be soft on the inside as well as the outside – so even the fur-covered Paro robot is not a Soft robot. Soft robotics research is about developing new soft, smart materials for both actuation and sensing (ideally within the same material). Soft robots would have the huge advantage over conventional stiff metal and plastic robots, of being light and, well, soft. For robots designed to interact with humans that’s obviously a huge advantage because it makes the robot intrinsically much safer.

Soft robotics research is still at the exploratory stage, so there are not yet prefered materials and approaches. In our lab we are exploring several avenues, one is electroactive polymers (EAPs) for artificial muscles; another is the bio-mimetic 3D printed flexible artificial whisker. Another approach makes use of shape memory alloys to actuate octopus like limbs: here is a very nice YouTube movie from the EU OCTOPUS project. And perhaps one of the most unlikely but very promising approaches: exploiting fluid-solid phase changes in ground coffee to make a soft gripper: the Jaeger-Lipson coffee balloon gripper.

Let me elaborate a little more on the coffee balloon gripper. Based on the simple observation that when you buy vacuum-packed ground coffee the pack is completely solid, yet as soon as you cut open the pack and release the vacuum the ground coffee returns to its flowing fluid state. Heinrich Jaeger, Hod Lipson and co-workers put ground coffee into a latex balloon then, by controlling the vacuum via a pump, they demonstrate a gripper able to safely pick up and hold more or less any object. Here is a YouTube video showing this remarkable ability.

Almost any planetary exploration robot is likely to need a gripper to pick up or collect rock samples for analysis or collection (for return to Earth). Conventional robot grippers are complex mechanical devices that need very precise control in order to reliably pick up irregularly shaped and sized objects. That control is mechanically and computationally expensive, and problematical because of time delays if it has to be performed remotely from Earth. Something like the Jaeger-Lipson coffee balloon gripper would – I think – provide a much better solution. This soft gripper avoids the hard control and computation because the soft material adapts itself to the thing it is gripping; it’s a great example of what we call morphological computation.

The second example I suggested is inspired by work in our lab on bio-inspired touch sensing. Colleagues have developed a device called TACTIP – a soft flexible touch sensor that provides robots (or robot fingers) with very sensitive touch sensing capable of sensing both shape and texture. Importantly the sensing is done inside TACTIP, so the outside surface of the sensor can sustain damage without loss of sensing. Here is a very nice YouTube report on the TACTIP project.

It’s easy to see that giving planetary robots touch sensing could be useful, but there’s another possibility I outlined: the potential to allow Earth scientists to feel what the robot’s sensor is feeling. PhD student Callum Roke and his co-workers developed a system based on TACTIP for what we call remote tele-haptics. Here is a video clip demonstrating the idea:

Imagine being able to run your fingers across the surface of Mars, or directly feel the texture of a piece of asteroid rock without actually being there.

In robotics we are very interested in both kinds of learning, and methods for programming robots that can learn are well known. A method for individual learning is called reinforcement learning (RL). It’s a laborious process in which the robot tries out lots and lots of actions and gets feedback on whether each action helps or hinders the robot in getting closer to its goal – actions that help/hinder are re/de-inforced so the robot is more/less likely to try them again; it’s a bit like shouting “warm, hot, cold, colder…” in a hide-and-seek game. It’s fair to say that RL in robotics is pretty slow; robots are not good individual learners, but that’s because, in general, they have no prior knowledge. As a fair comparison think of how long it would take you to learn how to make fire from first principles if you had no idea that getting something hot may, if you have the right materials and are persistent, create fire, or that rubbing things together can make them hot. Roboticists are also very interested in developing robots that can learn socially, especially by imitation. Robots that you can program by showing them what to do (called programming by demonstration) clearly have a big advantage over robots that have to be explicitly programmed for each new skill.

Within the artificial culture project PhD student (now Dr) Mehmet Erbas developed a new way of combining social learning by imitation and individual reinforcement learning, and the paper setting out the method together with results from simulation and real robots has been published in the journal Adaptive Behavior. Let me explain the experiments with real robots, and what we have learned from them.

Here’s our experiment. We have two robots – called e-pucks. The inset shows a closeup. Each robot has its own compartment and must – using individual (reinforcement) learning – learn how to navigate from the top right hand corner, to the bottom left hand corner of its compartment. Learning this way is slow, taking hours. But in this experiment the robots also have the ability to learn socially, by watching each other. Every so often one of the robots will stop its individual learning and drive itself out of its own compartment, to the small opening at the bottom left of the other compartment. There it will stop and simply watch the other robot while it is learning, for a few minutes. Using a movement imitation algorithm the watching robot will (socially) learn a fragment of what the other robot is doing, then combine this knowledge into what it is individually learning. The robot then runs back to its own compartment and resumes its individual learning. We call the combination of social and individual learning ‘imitation-enhanced learning’.

In order to test the effectiveness of our new imitation-enhanced learning algorithm we first run the experiment with the imitation turned off, so the robots learn only individually. This gives us a baseline for comparison. We then run two experiments with imitation-enhanced learning. In the first we wait until one robot has completed its individual learning, so it is an ‘expert’; the other robot then learns – using its combination of individual learning and social learning from the expert. Not surprisingly, learning this way is faster.

This graph shows individual learning only as the solid black line, and imitation-enhanced learning from an expert as the dashed line. In both cases learning is more or less complete when the graphs transition from vertical to horizontal. We see that individual learning takes around 360 minutes (6 hours). With the benefit of an expert to watch, learning time drops to around 60 minutes.

The second experiment is even more interesting. Here we start the two robots at the same time, so that both are equally inexpert. Now you might think it wouldn’t help at all, but remarkably each robot learns faster when it can observe, from time to time, the other inexpert robot, than when learning entirely on its own. As the graph below shows, the speedup isn’t as dramatic – but imitation enhanced learning is still faster.

Think of it this way. It’s like two novice cooks, neither of whom knows how to make chicken soup. Each is trying to figure it out by trial and error but, from time to time, they can watch each other. Even though it’s pretty likely that each will copy some things that lead to worse chicken soup, on average and over time, each hapless cook will learn how to make chicken soup a bit faster than if they were learning entirely alone.

In the paper we analyse what’s going on when one robot imitates part of the semi-learned sequence of moves by the other. And here we see something completely unexpected. Because the robots imitate each other imperfectly – when one robot watches another and then tries to copy what it saw, the copy will not be perfect – from time to time, one inexpert robot will miscopy the other inexpert robot and the miscopy, by chance, helps it to learn. To use the chicken soup analogy: it’s as if you are spying on the other cook – try to copy what they’re doing but get it wrong and, by accident, end up with better chicken soup.

This is deeply interesting because it suggests that when we learn in groups, making mistakes – noisy social learning – can actually speed up learning for each individual and for the group as a whole.

Full Reference
Mehmet D Erbas, Alan FT Winfield, and Larry Bull (2013), Embodied imitation-enhanced reinforcement learning in multi-agent systems, Adaptive Behavior. Published online 29 August 2013. Download pdf (final draft)

Here are the slides of my keynote at last week’s excellent EUCog meeting: Social and Ethical Aspects of Cognitive Systems. And the talk itself is here, on YouTube.

The talk was in three parts.

Part 1: here I outline why and how roboticists must be ethical. This is essentially a recap of previous talks. I start with the societal context: the frustrating reality that even when we meet to discuss robot ethics this can be misinterpreted as scientists fear a revolt of killer robots. This kind of media reaction is just one part of three linked expectation gaps, in what I characterise as a crisis of expectations. I then outline a few ethical problems in robotics – just as examples. Here I argue it’s important to link safe and ethical behaviour – something that I return to later. Then I recap the five draft principles of robotics.

Part 2: here I ask the question: what if we could make ethical robots? I outline new thinking which brings together the idea of robots with internal models, with Dennett’s Tower of Generate and Test, as a way of making robots that can predict the consequences of their own actions. I then outline a generic control architecture for robot safety, even in unpredictable environments. The important thing about this approach is that the robot can generate next possible actions, test them in its internal model, and evaluate the safety consequences of each possible action. The unsafe actions are then inhibited – and the robot controller determines which of the remaining safe actions is chosen, using its usual action-selection mechanism. Then I argue that it is surprisingly easy to extend this architecture for ethical behaviour, to allow the robot to predict the robot actions that would minimise harm for a human in its environment. This appears to represent an implementation of Asimov’s 1st and 3rd laws. I outline the significant technical challenges that would need to be overcome to make this work.

But, assuming such a robot could be built, how ethical would it be? I suggest that with a subset of Asimovian ethics it probably wouldn’t satisfy an ethicist or moral philosopher. But, nevertheless – I argue there’s a good chance that such a minimally ethical robot could help to increase trust, in the robot, from its users.

Part 3: in the final part of the talk I conclude with some ethical questions. The first is: if we could build an ethical robot, are we ethically compelled to do so? Some argue that we have an ethical duty to try and build moral machines. I agree. But the counter argument, my second ethical question, is are there ethical hazards? Are we opening a kind of ethical Pandora’s box, by building robots that might have an implicit claim to rights, or responsibilities. I don’t mean that such a robot would ask for rights, but instead that, because it is has some moral agency, then we might think it should be accorded rights. I conclude that we should try and build ethical robots. The benefits I think far outweigh any ethical hazards, which in any event can, I think, be minimised.

*It was not so much an epiphany, as a slow conversion from sceptic to believer. I have long term collaborator Michael Fisher to thank for doggedly arguing with me that it was worth thinking deeply about how to build ethical robots.

Rod and I are invited to visit the Jet Propulsion Labs (JPL). It’s my second visit to JPL. But it turned into probably the most inspirational afternoon of my life. Let me explain.After the tour the good folks who were showing us round asked if I would like to meet some of the post-docs in the lab. As he put it: the fancy control room with the big wall screens is really for the senators and congressmen – this is where the real work gets done. So, while Rod went off to discuss stuff with his new Faculty colleagues I spent a couple of hours in a back room lab, with a Caltech post-doc working on – as he put it – a summer project. I’m ashamed to say I don’t recall his name so I’ll call him Josh. Very nice guy, a real southern californian dude.

Now, at this point, I should explain that there was a real buzz at JPL. Voyager 2, which had already more than met its mission objectives was now on course to Uranus and due to arrive in January 1986. It was clear that there was a significant amount of work in planning for that event. The first ever opportunity to take a close look at the seventh planet.

So, Josh is sitting at a bench and in front of him is a well-used Apple II computer. And behind the Apple II is a small display screen so old that the phosphor is burned. This used to happen with CRT computer screens – it’s the reason screen savers were invented. Beside the computer are notebooks and manuals, including prominently a piece of graph paper with a half-completed plot. Josh then starts to explain: one of the cameras on Voyager 2 has (they think) a tiny piece of grit* in the camera turntable – the mechanism that allows the camera to be panned. This space grit means that the turntable is not moving as freely as it should. It’s obviously extremely important that when Voyager gets to Uranus they need to be able to point the cameras accurately, so Josh’s project is to figure out how much torque is (now) needed to move the camera turntable to any desired position. In other words: re-calibrate the camera’s controller.

At this point I stop Josh. Let me get this straight: there’s a spacecraft further from earth, and flying faster, than any manmade object ever, and your summer project is to do experiments with one of its cameras, using your Apple II computer. Josh: yea, that’s right.

Josh then explains the process. He constructs a data packet on his Apple II, containing the control commands to address the camera’s turntable motor and to instruct the motor to drive the turntable. As soon as he’s happy that the data packet is correct, he then sends it – via theRS232 connection at the back of his Apple II – to a JPL computer (which, I guess would be a mainframe). That computer then, in turn, puts Josh’s data packet together with others, from other engineers and scientists also working on Voyager 2, after – I assume – carefully validating the correctness of these commands. Then the composite data packet is sent to theDeep Space Network (DSN) to be transmitted, via one of the DSNs big radio telescopes, to Voyager 2.

Then, some time later, the same data packet is received by Voyager 2, decoded and de-constructed and said camera turntable moves a little bit. The camera then sends back to Earth, again via a composite data packet, some feedback from the camera – the number of degrees the turntable moved. So a day or two later, via a mind-bogglingly complex process involving several radio telescopes and some very heavy duty error-correcting codes, the camera-turntable feedback arrives back at Josh’s desktop Apple II with the burned-phosphor screen. This is where the graph paper comes in. Josh picks up his pencil and plots another point on his camera-turntable calibration graph. He then repeats the process until the graph is complete. It clearly worked because six months later Voyager 2 produced remarkable images of Uranus and its moons.

This was, without doubt, the most fantastic lab experiment I’d ever seen. From his humble Apple II in Pasadena Josh was doing tests on a camera rig, on a spacecraft, about 1.7 billion miles away. For a Thunderbirds kid, I really was living in the future. And being a space-nerd I already had some idea of the engineering involved in NASA’s deep space missions, but that afternoon in 1985 really brought home to me the extraordinary systems engineering that made these missions possible. Given the very long project lifetimes – Voyager 2 was designed in the early 1970s, launched in 1977, and is still returning valuable science today – its engineers had to design for the long haul; missions that would extend over several generations. Systems design like this requires genius, farsightedness and technical risk taking. Engineering that still inspires me today.

*it later transpired that the problem was depleted lubricant, not space grit.

Why aren’t there more intelligent mobile robots in real world applications? It’s a good question, and one I’m often asked. The answer I give most often is that it’s because we’re still looking for that game changing killer app – the robotics equivalent of the spreadsheet for PCs. Sometimes I place the blame on a not-quite-yet-solved technical deficit – like poor sensing, or sensor fusion, or embedded AI; in other words, our intelligent robots are not yet smart enough. Or I might cite a not-fully-developed-capability, like robots not able to cope with unpredictable (i.e. human) environments, or we can’t yet assure that our robots are safe, and dependable.

Last week at euRathlon 2013 I realised that these answers are all wrong. Actually that would be giving myself credit where none is due. The answer to the question: why aren’t there more intelligent mobile robots in real world applications was pointed out by several of the presenters at the euRathlon 2013 workshop, but most notably by our keynote speaker Shinji Kawatsuma, from the Japan Atomic Energy Authority (JAEA). In an outstanding talk Kawatsuma explained, with disarming frankness, that, although his team had robots they were poorly prepared to use those robots in the Fukushima Daiichi NPP, because the systems for deployment were not in place. The robots are not enough. Just as important are procedures and protocols for robot deployment in an emergency; mobile infrastructure, including vehicles to bring the robots to the emergency, which are capable – as he vividly explained – of negotiating a road system choked with debris (from the Tsunami) and strained with other traffic (rescue workers and evacuees); integration with other emergency services; and, above all, robot operators trained, practised and confident to guide the robots through whatever hazards they will face in the epicentre of the disaster.

In summing up the lessons learned from robots at Fukushima, Shinji Kawatsuma offered this advice – actually it was more of a heartfelt plea: don’t build robots, build robot systems. And, he stressed, those systems must include operator training programmes. It was a powerful message for all of us at the workshop. Intelligent robots are endlessly fascinating machines, with all kinds of difficult design challenges, so it’s not surprising that our attention is focussed on the robots themselves. But we need to understand that real world robots are like movie stars – who (despite what they might think) wouldn’t be movie stars at all without the supporting cast, camera and sound crews, writers, composers, special effects people and countless other departments that make the film industry. Take the Mars rover Curiosity – an A-list movie star of robotics. Curiosity could not do its job without an extraordinary supporting infrastructure that, firstly, delivered her safely to the surface of Mars and, secondly, allows Curiosity’s operators to direct her planetary science exploration.

So, to return to my question why aren’t there more intelligent mobile robots in real world applications. The answer is plain. It’s because without supporting systems: infrastructure and skilled operators integrated and designed to meet the real world need, a robot – regardless of how innovative and intelligent it is – will never make the transition from the lab to the real world. Without those systems that robot will remain no more than a talented but undiscovered actor.

Hans-Arthur Marsiske, The use of robots in Fukushima: Shinji Kawatsuma Interview, Heise online, 25 September 2013 (in German).
K Nagatani, S Kiribayashi, Y Okada, K Otake, K Yoshida, S Tadokoro, T Nishimura, T Yoshida, E Koyanagi, M Fukushima and S Kawatsuma, Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots, Journal of Field Robotics, 30 (1), 44-63, 2013.

As the presenting co-author I gave a one-slide one-minute pitch for the work, and here is that slide.

The paper proposes a new conceptual framework for evolving robots, that we call the Triangle of Life. Let me outline what this means. But first a quick intro to evolutionary robotics. In my very short introduction to Robotics I wrote:

One of the most fascinating developments in robotics research in the last 20 years is evolutionary robotics. Evolutionary robotics is a new way of designing robots. It uses an automated process based on Darwinian artificial selection to create new robot designs. Selective breeding, as practised in human agriculture to create new improved varieties of crops, or farm animals, is (at least for now) impossible for real robots. Instead, evolutionary robotics makes use of an abstract version of artificial selection in which most of the process occurs within a computer. This abstract process is called a genetic algorithm. In evolutionary robotics we represent the robot that we want to evolve, with an artificial genome. Rather like DNA, our artificial genome contains a sequence of symbols but, unlike DNA, each symbol represents (or ‘codes for’) some part of the robot. In evolutionary robotics we rarely evolve every single part of a robot.

A robot consists of a physical body with an embedded control system – normally a microprocessor running control software. Without that control software the robot just wouldn’t do anything – it would be the robot equivalent of a physical body without a mind. In biological evolution bodies and minds co-evolved (although the dynamics of that co-evolutionary process are complex and interesting). But in 20 years or so of evolutionary robotics the vast majority of work has focussed only on evolving the robot’s controller. In other words we take a pre-designed robot body, then use the genetic algorithm to discover a good controller for that particular body. There has been little work on body-brain co-evolution, and even less work on evolving real robot bodies. In fact, we can count the number of projects that have evolved new physical robot bodies on the fingers of one hand*. Here is one of those very rare projects: the remarkable Golem project of Hod Lipson and Jordan Pollack.

This is surprising. When we think of biological evolution and the origin of species, our first thoughts are of the evolution and diversity of body shapes and structures. In the same way, the thing about a robot that immediately captures our attention is its physical body. And bodies are not just vessels for minds. As Rolf Pfeifer and Josh Bongard explain in their terrific book How the Body Shapes the Way We Think, minds depend crucially on bodies. The old dogma of Artificial Intelligence, that we can simply design an artificial brain without any regard to its embodiment, is wrong. True artificial intelligence will only be achieved by co-evolving physical bodies with their artificial minds.

In this paper we are arguing for a radical new approach in which the whole process of co-evolving robot bodies and their controllers takes place in real space and real time. And, as the title makes clear, we are also advocating a open-ended cycle of artificial life, in which every part of the robots’ artificial life cycle takes place in real space and real time, from artificial conception, through to artificial birth, artificial infancy and development, then artificial maturity and mating. Of course these words are metaphors: the artificial processes are at best a crude analogue. But let me stress that no-one has demonstrated this. The examples that we give in the paper, from the EU Symbrion project, are just fragments of the process – not joined up in reality. And the Symbrion example is very constrained because of the modular robotics approach which means that the building blocks of these ‘multi-cellular’ robot organisms – the ‘cells’ – are themselves quite chunky robots; we have only 3 cell types and only a handful of cells for evolution to work with. Evolving robots in real space and real time is ferociously hard but, as the paper concludes: Our proposed artificial life system could be used to investigate novel evolutionary processes, not so much to model biological evolution – life as it is, but instead to study life as it could be.

Full reference:

Eiben AE, Bredeche N, Hoogendoorn M, Stradner J, Timmis J, Tyrrell A, and Winfield A (2013), The Triangle of Life: Evolving Robots in Real-time and Real-space, pp 1056-1063 in Advances in Artificial Life, ECAL 2013, proc. Twelfth European Conference on the Synthesis and Simulation of Living Systems, eds. Liò P, Miglino O, Nicosia G, Nolfi S and Pavone M, MIT Press.

*I was surprised to discover this when searching the literature for a new book chapter in co-authoring on Evolvable Robot Hardware.

Related blog posts:
New experiments in embodied evolutionary swarm robotics
New video of 20 evolving e-pucks

But the thing I’ve noticed in recent months is that I have memories of being in places in Skyrim that, subjectively, feel completely indistinguishable from memories of being in real places in the real world. In other words the quality and character of the memory – the sense of having really been there – is no different when I recall, say, standing on the porch of Dragonsreach Hall looking South toward Throat of the World mountain, as when I remember looking down toward the Cumberland basin from the south footpath of the Clifton suspension bridge.

For me this is a new experience. I’ve been playing (and sometimes coding) video games since that meant batting a pixelated ball from one side of a low-res monochrome screen to the other on home built 8-bit micros in the 1980s. I have fond memories of playing the first generation Alone in the Dark ~1993 on a 386 PC, with my 5 year old son – but my memories are of the experience of playing the game with Tom, not of actually being in that haunted house. More recent games on the Xbox 360, with graphics I would have had difficulty imagining 20 years ago, have not had the same effect of creating such compellingly real memories for me.

So what is it about Skyrim that is making these memories feel so real? I think there are several factors. The first is that the scenery is so breathtakingly beautiful, which means you really do want to just stop and stare for awhile. Second, and equally important I think, is that this is not some imagined alien landscape. It is decidedly Earth – a cold Northerly Earth certainly, but the fells and mountains, the lakes and forests, the grasses and especially trees, are realised so accurately you can identify whether it’s a birch or an oak. Third, the landscape is in constant motion – so the grasses sway in the wind, the brooks gurgle and splash and insects flutter. Wait a little longer and you realise the day is passing from afternoon to dusk, the sky turns golden in the sunset, then to night. A star spangled and moon-crossed night sky then rewards the patient (and the brave – this is a wild and dangerous place), following by a glorious sunrise. And there is weather too. Rain, which splashes delightfully on the lake, occasional thunderstorms (learn the power and you can call them up!), and snow blizzarding in the mountains. In case you can’t go there yourself watch this YouTube movie: Skyrim – landscapes and scenery.

All of these are I think important cues in making the experience, and hence the memory, feel so real. But I think there is another factor, which is that my journeying through Skyrim is part of a narrative which connects places with events, quests and discoveries. So the memorable places are those I have arrived at following some perilous and occasionally epic trail, with multiple trials on the journey, Or they are places I have discovered offer safety and refuge; places to return to after days questing in the wilderness. Perhaps the depth and intensity of the experienced narrative somehow makes up for the limitations of the sensory experience? With only 2D vision on a flat TV screen and stereo audio, and nothing at all to stimulate the rest of the considerable human sensorium, it seems incredible that such a weakly immersive experience – compared to being in real places – can create subjectively comparable memories.

Since thinking about these memories of Skyrim, I’ve wondered if these are technically false memories. Am I experiencing so called False Memory Syndrome? Although FSM is controversial what is beyond doubt is the extraordinary suggestibility and unreliability of human memory. I was astonished by the unreliability of memory I witnessed at first hand two years ago while on Jury service. But false memories are memories of events that never happened, but are strongly believed. On reflection I think my memories of Skyrim do not fall into this category. The events and places occurred in the virtual rather than the real, but they, and my experience of them, really happened.

So if immersive video games technology has reached the point that it can create, for the gamer, memories of places and events which feel no different to memories of places and events in the real world, is this a bad thing? And, as the technology improves to make the experience more immersive, involving more senses, will we find ourselves unable to distinguish between the virtual and the real – confusing memories of one with the other? I think the answer is no. After all we each create a personal narrative – the remembered story of our lives – and I don’t think it matters whether the events that make up that story happen in the virtual or the real. I think we’re just as able to recall the difference between a trip to Rhyll and Rome, as between Skegness and Skyrim. And if, with advancing years or just because that’s the way we are, we start to confuse these memories I don’t think we’re any more likely to confuse the virtual and the real, as we are the real and the real. Nor do I think the degree of immersion in the virtual matters as far as memories of being there is concerned*. After all, being in the real world is a fully immersive experience. Even if, and when, we can climb into a full-body immersive gaming rig, like those of Ernest Cline’s brilliant Ready Player One, we will still only have an experience equal to that of the real world. So why should those experiences be remembered any differently to those in the real?

Ok. I’ve persuaded myself there isn’t a problem. Time for another trip to Skyrim.

*There is of course another quite different concern – to do with how much more addictive the experience will become.Will we neglect the real – and ourselves, like Larry Niven’s wireheads?

When a robot ethics working group met nearly three years ago one of the things we fretted about was privacy. We were concerned especially about personal companion robots. Whatever their purpose, be it healthcare for the elderly or disabled, or childcare, or simply Robot and Frank -style companionship, we debated the inevitable privacy issues of this kind of robot. Two aspects directly impact privacy, since personal companion robots are likely to (1) collect and store data and (2) be networked.

We also worried about hacking. The third of our Principles of robotics states: Robots are products: as with other products, they should be designed to be safe and secure. And the commentary for this principle has this to say about hacking:

We are aware that the public knows that software and computers can be “hacked” by outsiders, and processes need to be developed to show that robots are secure as far as possible from such attacks.

It seems our concerns were well founded. Last week a report appeared of a networked baby monitor that was apparently hacked. It was pretty distressing. The hacker was shouting abuse at the baby, chillingly using her name – it seems that he (let’s assume it was a he) was able to gain access to the baby monitor’s video feed and read the baby’s name displayed above her bed. Even more chilling (adding to the parent’s horror) the execrable hacker then turned the camera to look at them when they entered their child’s room to find out what was going on.

A WiFi IP Camera, aka Baby Monitor is, I contend, a teleoperated robot. The thing that makes it a robot is the motorised pan and tilt mechanism for steering the camera. So, despite password protection, Mr Gilbert’s networked robot was hacked. This particular robot, and I hazard tens of thousands like it, is absolutely not secure from attack.

The consequences of this particular attack were, fortunately, not much more serious than giving the Gilbert’s a fright they surely won’t forget for some time. But for me one particularly egregious aspect of this robot hack – something that the robot ethics working group did not anticipate – was the verbal abuse hurled at baby Allyson and her parents. It is with profound dismay that I ask the question: is the first case of RoboTrolling..?

Richard planted a seed that’s been growing since. What I didn’t fully appreciate then, but do now, is the profound importance of narrative. More than we perhaps imagine. Narrative is, I suspect, a fundamental property of both human societies and individual human beings. It may even be a universal property of all advanced societies of sentient social beings.

Let me try and justify this outlandish claim. First, take human societies. We humans love to tell each other stories. Whether our stories are epic poems, love songs; stories told with sound (music), or movement (dance), or with stuff (sculpture or art). Stories about what we did today, or on our holidays, stories made with images (photos, or movies); true stories or fantasies, or stories about the Universe that strive to be true (science), or very formal abstract stories told with mathematics, stories are everywhere. Arguably, most of human culture consists of stories.

Since humans started remembering stories and passing them on orally, and more recently with writing, we have had history: the more-or-less-true grand stories of human civilisation. Even the many artefacts of our civilisation are kinds of stories. They are embodied stories, which narrate the process by which they were designed and made; the plans and drawings which we use to formally record those designs are literally stories which tell how to arrange and join materials in space to fashion the artefact. Project plans are narratives of a different kind: they tell the story of the future steps that must be taken to achieve a goal. Computer programs are stories too. Except that they contain multiple narratives (bifurcated with branches and reiterated with loops), whose paths are determined by input data, which are related over and over at blinding speed within the computer. 

Now consider individual humans. There is a persuasive view in psychology that each of us owes our identity, our sense of self, to our personal life stories. The physical stuff that makes us, the cells of our body, are regenerated and replaced continuously, so that there’s very little of you that existed 5 years ago. (I just realised the fillings in my teeth are probably the oldest part of me!) Yet you are still you. You feel like the same you 10, 20 or in my case 50 years ago – since I first became self-aware. I think that it’s the lived and remembered personal narrative of our lives that provides us with the feeling, the illusion if you like, of a persistent self. This is I think why degenerative brain diseases are so terrifying. They appear to eat away that personal narrative so devastatingly that the person is ultimately lost, even while their physical body continues living.

I was tremendously excited to be invited to a cross-disciplinary workshop on Narrative and Complex Systems at the York Centre for Complex Systems Analysis, co-organised by York Professors of English Richard Walsh, and Computer Science Susan Stepney. For the first time I found myself in a forum in which I could share and debate ideas on narrative.

In preparing for the workshop I realised that perhaps the idea of robots telling each other stories isn’t as far fetched as it first appears. Think about a simple robot, like the e-puck. What does the story of its life consist of? Well, it is the complete history of all of the movements, including turns, etc, punctuated by interactions with its environment. Because the robot and its set of behaviours is simple, then those interactions are pretty simple too. It occurred to me that it is perfectly possible for a robot to remember everything that has ever happened to it. Now place a number of these robots together, in a simple ‘society’ of robots, and provide them with the mechanism to exchange ‘life stories’ (or more likely, fragments of life stories). This mechanism is something we already developed in the Artificial Culture project – it is social learning by imitation. These robots would be telling each other stories.

But, I hear you ask, would these stories have any meaning? Well, to start with I think we must abandon the notion that they would necessarily mean anything to us humans. After all, these are robots telling each other stories. OK, so would the stories mean anything to the robots themselves, especially robots with limited ‘cognition’? Now we are in the interesting territory of semiotics, or – to be more accurate – robosemiotics. What, for instance, would one robot’s story signify to another? That signification would I think be the meaning. But I think to go any further we would need to do the robot experiment I have outlined here.

And what would be the point of my proposed robot experiment? It is, I suggest, this:

To explore, with an abstract but embodied model, the relationship between the narrative self and shared narrative, i.e. culture.

By doing this experiment would we be, as Richard Gregory suggested, really onto something?

[This post originally appeared on Alan Winfield’s blog on July 24, 2012.]

At the excellent European Robotics Forum earlier this year, I attended a workshop called AI meets Robotics. The thinking behind the workshop was:

The fields of Artificial Intelligence (AI) and Robotics were strongly connected in the early days of AI, but became mostly disconnected later on. While there are several attempts at tackling them together, these attempts remain isolated points in a landscape whose overall structure and extent is not clear. Recently, it was suggested that even the otherwise successful EC program “Cognitive systems and robotics” was not entirely effective in putting together the two sides of cognitive systems and of robotics.

I couldn’t agree more. Actually I would go further and suggest that robotics has a much bigger problem than we think. It’s a new kind of dualism which parallels Cartesian brain-mind dualism, except in robotics, it’s hardware-software dualism. And like Cartesian dualism it could prove just as unhelpful, both conceptually, and practically – in our quest to build intelligent robots.

While sitting in the workshop last week I realised rather sheepishly that I’m guilty of the same kind of dualistic thinking. In my Introduction to Robotics one of the (three) ways I define a robot is: an embodied Artificial Intelligence. And I go on to explain:

…a robot is an Artificial Intelligence (AI) with a physical body. The AI is the thing that provides the robot with its purposefulness of action, its cognition; without the AI the robot would just be a useless mechanical shell. A robot’s body is made of mechanical and electronic parts, including a microcomputer, and the AI made by the software running in the microcomputer. The robot analogue of mind/body is software/hardware. A robot’s software – its programming – is the thing that determines how intelligently it behaves, or whether it behaves at all.

But, as I said in the workshop, we must stop thinking of cognitive robots as either “a robot body with added AI”, or “an AI with added motors and sensors”. Instead we need a new kind of holistic approach that explicitly seeks to avoid this lazy “with added” thinking.

[This post originally appeared on Alan Winfield’s blog on March 24, 2013.]

A week ago the DARPA Robotics Challenge unveiled the ATLAS humanoid robot, which will be used by seven competing teams. Developed by Boston Dynamics, ATLAS is an imposing 1.8m 150Kg bipedal humanoid robot, powered via a tethered cable. Another six teams have designed their own robots, and interestingly five of these are humanoid, and one a four-limbed simian-inspired robot.

In the euRathlon project we are taking a different approach in that we don’t expect, or require, the competing robots to be humanoid or zoomorphic. None of the euRathlon competition scenarios demand a humanoid robot to, for example, be able to step inside a vehicle and drive it. However, for the land robots at least, there is nothing stopping euRathlon teams from bringing humanoid robots to the competition.

As I wrote when we launched euRathlon early this year, the big vision of euRathlon is a competition scenario in which no single type of robot is, on its own, sufficient. Inspired by the Fukushima accident of March 2011, the 2015 euRathlon competition will require teams of land, sea and flying robots to autonomously cooperate, survey the scene, identify critical hazards and undertake tasks to make the plant safe. Leading up to this grand challenge in 2015, will be related and preparatory land and underwater robot competitions in 2013 and 2014, respectively.

The difference of our approach is not the result of an in-principle decision. Rather, it flows naturally from several factors. First, we are specifically creating competition scenarios that require cooperating teams across the three domains of land, sea and air. Second, we are looking for very high levels of autonomy, so the robot teams will, ideally, complete their mission with hands-off human monitoring only. Any human interventions will be penalised in the euRathlon scoring schema. And third, we are not looking to push innovation in the robot platforms themselves, but rather in their cognition, autonomy and system level team working. Thus, euRathlon teams who make use of existing and proven robot hardware will gain a big advantage in that they can focus all of their efforts on the software, communications and systems engineering; the AI and the autonomy. And by autonomy we mean both control and energy autonomy. The euRathlon competition scenarios preclude the use of tethered power connections, so robots must carry their own energy supplies sufficient to last the whole mission.

For these reasons the euRathlon robots are likely to look rather conventional: wheeled or tracked land robots; fixed or rotary wing (i.e. quadcopter) flying robots, and ROV-type underwater robots. Not as dramatic as the DARPA robot challenge humanoid or animal-like robots perhaps, but looks can be deceptive: the real innovation in the euRathlon robots will be in the autonomous cooperation across the three domains. Something that has not been demonstrated in realistic outdoor disaster response scenarios.

Of course there is nothing to stop euRathlon teams from using a bio-mimetic approach, so fish-like underwater robot cooperate with bird-like flying robots, and legged animal-like land robots. That really would be something!

Related blog posts:
euRathlon is go! (Feb 2013)
Real-world robotics reality check (May 2010)
A truly Grand Challenge (August 2007)

The key messages from my talk were:

• The new wave of robotics represents a kind of Cambrian explosion in robotics: an exciting but also bewildering exploration of new forms, functions and materials. This explosion of diversity means that the New Robotics is not one kind of robot. Thus any kind of prediction about which of these will successfully evolve to become mainstream is more or less impossible.

• There are two common myths: first, the waiting-for-AI myth: the idea that robotics is waiting for some breakthrough innovation in Artificial Intelligence, without which robotics is stuck. And second, the need-full-autonomy myth: the idea that fully autonomous robots represent some ideal end-state of the development of robotics; this is not true – instead we need robots and human-robot interfaces that will transition smoothly between tele-operation and semi-autonomy. We call this dynamic autonomy.

• There are significant opportunities for innovation right now – underpinned by a significant head-of-steam of fundamental technologies from university R&D. I offer some examples for discussion, including companion robots, wearable robots and tele-operated robots with immersive tele-presence, perhaps making use of remote tele-haptics (although I claim no special insights).

• We need new and agile approaches to innovation. New kinds of research-industry partnerships and flexible, responsive pathways to commercialisation. Especially campus start-ups and incubators, nurturing post-docs as next generation entrepreneurs; and innovative modes of funding. We also need responsible and sustainable innovation.Haptocs

Here are links to further information, and video clips, on the projects and robots highlighted in the talk:

Slide 10: The Cooperative Human Robot Interaction Systems (CHRIS) project

• Project web pages: www.chrisfp7.eu
• YouTube movie showing CHRIS robots including BERT, from BackToThePlanetVideo

Slide 11: MOBISERV – An Integrated Intelligent Home Environment for the Provision of Health, Nutrition and Well-Being Services to Older Adults

• Project web pages: www.mobiserv.info
• YouTube movie showing MOBISERV concepts

Slide 12:Hand exoskeleton for post stroke recovery

Slide 13: Tactile Sensing – tele-haptics

Slide 14: Surgical Haptics

• Haptics for tele-surgery
• Palpating surgical gripper

Slide 15: Search and Rescue – Disaster Response

• Project web pages: www.eurathlon.eu

Slide 16: Towards energy sustainability

• The BRL Bioenergy & Self Sustainable Systems theme

But I understand the sentiment behind the question. In robotics we have a shared sense of a technology that has yet to reach its true potential; of a dream unfulfilled.

The question asks what is the single biggest obstacle. In my view some of the biggest  immediate obstacles are not technical but human. Let me explain with an example. We already have some very capable tele-operated robots for disaster response. They are rugged, reliable and some are well field-tested. Yet why it is that robots like these are not standard equipment with fire brigades? I see no technical reason that fire tenders shouldn’t have, as standard, a compartment with a tele-operated robot – charged and ready for use when it’s needed. There are, in my view, no real technical obstacles. The problem I think is that such robots need to become accepted by fire departments and the fire fighters themselves, with all that this entails for training, in-use experience and revised operational procedures.

In the longer term we need to ask what it would mean for robotics to go mainstream. Would it mean everyone having a personal robot, in the same we all now have personal computing devices? Or, when all cars are driverless perhaps? Or, when everyone whose lives would be improved with a robot assistant, could reasonably expect to be able to afford one? Some versions of mainstream are maybe not a good idea: I’m not sure I want to contemplate a world in there are as many personal mobile robots, as there are mobile phones now (~4.5 billion). Would this create robot smog, as Illah Nourbakhsh calls it in his new book Robot Futures?

Right now I don’t have a clear idea of what it would mean for robots to go mainstream, but one thing’s for sure: we should be thinking about what kind of sustainable, humanity benefitting and life enhancing mainstream robot futures we really want.

Read more answers →

Brian Gerkey on “What is the single biggest obstacle preventing robotics from going mainstream?”

The biggest obstacle to broader adoption of robotics is that only experienced roboticists can develop robotics applications. To make a robot reliably and robustly do something useful, you need a deep understanding of a broad variety of topics, from state estimation to perception to path planning …

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Mark Tilden on “What is the single biggest obstacle preventing robotics from going mainstream?”

From experience, the single biggest obstacle to personal robotic markets is cost, both in money and time. Robots have the disadvantage of being over-promoted in fictional media while over-priced on the shelves. Sci-fi is fine to inspire if builders feel the money-time is justified, but …

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Alan Winfield on “What is the single biggest obstacle preventing robotics from going mainstream?”

Well it depends on what you mean by mainstream. For a number of  major industry sectors robotics is already mainstream. In assembly-line automation, for instance; or undersea oil well maintenance and inspection …

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Perhaps a better example is this: The act of picking up a cup of hot coffee and bringing it to your lips appears, on the face of it, to be perfectly observable. No mystery at all. We can see the joints and muscles at work, ‘feel’ the tactile sensing of the coffee cup, and its weight as we begin to lift it. We can even build mathematical models of the kinetics and dynamics, and (with somewhat more difficulty) make robot arms to pick up cups of coffee. But – I contend – we are kidding ourselves if we think we know what’s going on in the complex sensory and neurological processes that appear so effortless to perform. The fact we can observe and even feel ourselves lifting a coffee cup gives very little real insight. And the mathematical models – and robots – are not really models of the human neurological and physiological processes at all, they are models of idealised abstractions of limbs, joints and hand.

I would argue that we have no greater insight into the workings of this (apparently straightforward) physical act, than we do of thinking itself. But again this is not surprising. The additional cognitive machinery to be able to access or experience the inner workings of any process, whether mental or physical, would be huge and (biologically) expensive. And with no apparent survival value (except perhaps for philosophers of mind), it’s not surprising that such mechanisms have not evolved. They would of course require not just extra grey matter, but sensing too. It’s interesting that there are no pain receptors within our brains – that’s why it’s perfectly possible to have brain surgery while wide awake.

But this got me thinking. Imagine that at some future time we have nanoscale sensors capable of positioning themselves throughout our brains in order to provide a very large sensor network. If each sensor is monitoring the activity of key neurons, or axons, and able to transmit its readings in real-time to an external device, then we would have the data to provide ourselves with a real-time activity image of our own brains. It could be presented visually, or perhaps sonically (or via multi-media). It might be fun for awhile, but this personal brain imaging technology (let’s call it iBrain) probably wouldn’t provide us with much more insight or experience of our own thought processes.

But let’s assume that by the time we have the nanotechnology for harmlessly inserting millions of brain nanosensors we will have also figured out the major architectural structures of the brain – crucially linking the neural scale to the macro scale. Actually, if we believe that the recently announced European and US human brain Grand Challenges will achieve what they are promising in terms of modelling and mapping human brain activity, then such an understanding should only be a few decades away. So now build those maps and structures into the personal iBrain, and we will be presented not with a vast and bewildering cloud of colours, as in the beautiful image above, but a simpler image with major highways and structures highlighted. Still complex of course, but then so are street maps of cities or countries. So the iBrain would allow you to zoom into certain regions and really see what’s going on while you (say) listen to Bach (the very thing I’m doing right now).

Then we really would be able to observe our own brains at work and, just perhaps, experience the connection between brain and thought.

This post originally appeared on Alan Winfield’s Web Log on Feb. 20, 2013.

Public Expectations
Here’s a simple experiment. Ask a non-roboticist to give you an example of a robot — the first that comes into their mind. The odds are that it will be a robot from a Science Fiction movie: perhaps Terminator, R2-D2 or C-3PO or Data from Star Trek. Then ask them to name a real-world robot. Unlike your first question, which they will have answered quickly, this one generally needs a little longer. You might get an answer like “the robot in the advert that spray-paints cars” or, if you’re lucky, they might know someone with a robot vacuum cleaner. So, although most people have a general idea that there are robots in factories, or helping soldiers to defuse bombs, the robots that they are most familiar with – the ones they can name and describe – are fictional.

None of this is surprising. The modern idea of a robot was, after all, conceived in Science Fiction. Czech playwright K. Capek first used the word Robot to describe a humanoid automaton in his play Rossum’s Universal Robots (RUR) and Isaac Asimov was the first to coin the word Robotics, in his famous short stories of the 1940s. The idea of a robot as an artificial mechanical person has become a ubiquitous fictional trope, and robots have, for half a century, been firmly rooted in our cultural landscape. We even talk about people acting robotically and, curiously, we don’t mean like servants, we mean in a fashion that mimics the archetypal robot: stiff jointed and emotionally expressionless.

Furthermore, people like robots, as anyone who has had the pleasure of giving public talks will know. People like robots because robots are, to paraphrase W. Grey Walter, An Imitation of Life. Which probably accounts for the observation that we are all, it seems, both fascinated and disturbed by robots in equal measure. I have often been asked the question “how intelligent are intelligent robots?”, but there’s always an unspoken rider “… and should we be worried?”. Robot dystopias, from Terminator, to The Matrix or out-of-control AI like HAL in Kubrick’s 2001, make compelling entertainment but undoubtedly feed the dark side of our cultural love affair with robots.

It is not surprising then, that most people’s expectations about robots are wrong. Their beliefs about what real-world robots do now are hazy, and their expectations about what robots might be like in the near future often spectacularly over optimistic. Some think that real-world robots are just like movie robots. Others are disappointed and feel that robotics has failed to deliver the promises of the 1960s. This expectation gap — the gap between what people think robots are capable of and what they’re really capable of — is not one-dimensional and is, I argue, a problem for the robotics community. It is a problem that can manifest itself directly when, for instance, public attitudes towards robots are surveyed and the results used to inform policy [1]. It makes our work as roboticists harder, because many people think that the hard problems we are working on are already solved, and because it creates societal expectations of robotics that cannot be met. And it is a problem because it underpins the next expectation gap I will describe.

Press and Media Expectations
You are technically literate, an engineer or scientist perhaps with a particular interest in robotics, but you’ve been stranded on a desert island for the past 30 years. Rescued and returned to civilisation you are keen to find out how far robotics science and technology has advanced and, rejoicing in the marvellous inventions of the Internet and its search engines, you scour the science press for robot news. Scanning the headlines you are thrilled to discover that robots are alive, and sending messages from space; robots can think or are “capable of human reasoning or learning”; robots have feelings, relate to humans, or demonstrate love, even behave ethically. Truly robots have achieved their promised potential.

Then of course you start to dig deeper and read the science behind these stories. The truth dawns. Although the robotics you are reading about is significant work, done by very good people, the fact is — you begin to realise — that now, in 2013, robots cannot properly be said to think, feel, empathise, love or be moral agents; and certainly no robot is, in any meaningful sense, alive or sentient. Of course your disappointment is tempered by the discovery that astonishing strides have nevertheless been made.
So, robotics is subject to journalistic hype. In this respect robotics is not unique. Ben Goldacre has done much to expose bad science reporting, especially in medicine. But a robot is different to, say, a new strain of MRSA because — as I outlined above — most people think they know what a robot is. Goldacre has characterised bad science stories as falling into three categories: wacky stories, scare stories and breakthrough stories [2]. My observation is that robots in the press most often headline as either wacky or scary, even when the development is highly innovative.

I believe that robohype is a serious problem and an issue that the robotics community should worry about. The problem is this: most people who read the press reports are lay readers who — perfectly reasonably — will not read much beyond the headline; certainly few will look for the source research. So every time a piece of robohype appears (pretty much every day) the level of mass-delusion about what robots do increases a bit more, and the expectation gap ratchets a little wider. Remember that the expectation gap is already wide. We are at the same time fascinated and fearful of robots, and this fascination feeds the hype because we want (or dread) the robofiction to become true. Which is of course one of the reasons for the hype in the first place.

Who’s to blame for the robohype? Well, we roboticists must share the blame. When we describe our robots and what they do we use anthropocentric words, especially when trying to explain our work to people outside the robotics community. Within the robotics and AI community we all understand that when we talk about an intelligent robot, what we mean is a robot that behaves as if it were intelligent; ‘intelligent robot’ is just a convenient shorthand. So when we talk to journalists, we should not be too surprised when “this robot behaves, in some limited sense, as if it has feelings” gets reported as “this robot has feelings”. But science journalists must, I think, also do better than this.

Funder and Stakeholder Expectations
Many of us rely on research grants to fund our work and — whether we like it or not — we have to become expert in the discipline of grantology. We pore over the small print of funding calls and craft our proposals with infinite care in an effort to persuade reviewers (also skilled grantologists) to award the coveted ‘outstanding’ scores. We are competing for a share of a limited resource, and the most persuasive proposals — the most adventurous, which also promise the greatest impact while matching themes defined to be of national importance — tend to succeed. Of course all of this is more or less equally true whether you are bidding for a grant in history, microbiology or robotics. But the crisis of expectations makes robotics different.

There are, I think, three factors at work. The first is the societal and cultural context — the expectation gaps I have outlined above. The second is the arguably disgraceful lack of useful and widely accepted benchmarks in robotics, which means that it is perfectly possible to spend 3 years developing a new robot which is impossible to quantifiably demonstrate as superior to comparable robots, including those that already existed when that project started. And the third is the fact that policymakers, funders and stakeholders are themselves under pressure to deliver solutions to very serious societal or economic challenges and therefore perhaps too eager to buy into the promise of robotics. Whether naively or wittingly, we roboticists are I believe guilty of exploiting these three factors when we write our grant applications.

I believe we now find ourselves in an environment in which it is now almost de rigueur to over-promise when writing grant applications. Only the bravest proposal writer will be brutally honest about the extreme difficulty of making significant progress in, for instance, robot cognition and admit that even a successful project, which incrementally extends the state of the art, may have only modest impact. Of course I am not suggesting that all grants over promise and under deliver, but I contend that many do and – because of the factors I have set out – they are rarely called to account. Clearly the danger is that sooner or later funding bodies will react by closing down robotics research initiatives and we will enter a new cycle of AI Winter.

AI has experienced “several cycles of hype, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later” [3]. The most serious AI Winter in the UK was triggered by the Lighthill Report [4] which led to a more or less complete cancellation of AI research in 1974. Are we heading for a robotics winter? Perhaps not. One positive sign is the identification of Robotics and Autonomous Systems as one of eight technologies of strategic importance to the UK [5]. Another is the apparent health of robotics funding in the EU and, in particular, Horizon 2020. But a funding winter is only the most extreme consequence of the culture of over-promising I have outlined here.

Discussion
I want to conclude this talk with some thoughts on how we, as a community, should respond to the crisis of expectations. And respond we must. We have, I believe, an ethical duty to the society we serve, as well as to ourselves, to take steps to counter the expectation gaps that I have outlined. Those steps might include:

• At every opportunity, individually and collectively, we engage the public in honest explanation and open dialogue to raise awareness of the reality of robotics. We need to be truthful about the limitations of robots and robot intelligence, and measured with our predictions. We can show that real robots are both very different and much more surprising than their fictional counterparts;
• When we come across particularly egregious robot reporting in the press and media we make the effort to contact the reporting journalist, to explain simply and plainly the true significance of the work behind the story.
• Individually and collectively we endeavour to resist the pressure to over-promise in our bids and proposals, and when we review proposals or find ourselves advising on funding directions or priorities, we seek to influence towards a more measured and ultimately sustainable approach to the long term Robotics Project.

References
[1] Public Attitudes towards Robots. Special Eurobarometer 382, European Commission, 2012.

[2] Ben Goldacre. Don’t dumb me down. The Guardian, 8 September 2005.

[3] AI Winter. Wikipedia, accessed 14 March 2013.

I’m very excited to be leading a new project called euRathlon – which is short for European Robotics Athlon. Up until now the project has been under wraps, but now – finally – we can go public. I’ll explain a bit more about the process that led to here later in this blog post, but first – about euRathlon.

It is an EU funded project to set up and run a series of outdoor robotics competitions. The focus is robots for search and rescue, or – more broadly – disaster response. Right now robots are not part of the standard equipment of emergency services, like fire brigades. But actually, robotics technology is coming close to the point where they could be and, in my view, should be. It seems to me that first responders should have robots as a standard part of their equipment, so that when there is a disaster robots are used as a matter of routine. euRathlon will, I hope, speed up the development and adoption of smarter robots for first responders.

The big vision of euRathlon is a competition scenario in which no single type of robot is, on its own, sufficient. Inspired by the Fukushima accident of 2011, our Grand Challenge will require teams of land, sea and flying robots to investigate the scene. Here is the project abstract:

euRathlon is a new outdoor robotics competition, which will invite teams to test the intelligence and autonomy of their robots in realistic mock emergency-response scenarios. Inspired by the 2011 Fukushima accident the euRathlon competition will require a team of land, underwater and flying robots to work together to survey the scene, collect environmental data, and identify critical hazards. Leading up to this ‘grand challenge’ in 2015, will be directly related land and underwater robot competitions in 2013 and 2014, respectively. The euRathlon competitions will be supported by annual workshops for competitors. In parallel there will be an open process of developing benchmarks to allow comparison of different robots in the euRathlon competitions. Linked public engagement activities will connect euRathlon with robotics research, industry and emergency services, as well as the general public. Attendance of spectators will be welcomed, and we hope that euRathlon events will attract considerable press and media attention. By targeting a specific and urgent need – intelligent robots for disaster-response -euRathlon will provide European robotics with a platform for challenging, extending and showcasing European cognitive robotics technologies.

Followers of this blog will know that I’ve been involved in the European Land Robotics Challenge (ELROB) for some years. I blogged about it in 2010: Real-world robotics reality check, and in 2007: A truly Grand Challenge. So, when the EU Framework Programme (FP7) issued a call for competition proposals late in 2011 an opportunity arose for those of us involved in ELROB to think about bidding for a new competition, building on that experience and extending our ambition. We were very fortunate to link up with the organisers of the Student Autonomous Underwater Challenge – Europe (SAUC-E), a very well regarded underwater robot competition. We then had land and sea robots covered. The final piece of the jigsaw fell into place when we were joined by our final partner, organisers of the workshop on Research, Development and Education on Unmanned Aerial Systems (RED-UAS 2011), with huge experience of aerial robots.

The euRathlon consortium was complete, and together we submitted our bid in April 2012. Following evaluation the bid was successful and then, from September to December 2012, we went into a phase of project negotiation, in which we worked out and agreed the details of the project with the EC. That process concluded successfully, and the project started on 1 January 2013.

So now, euRathlon is go!

This was a question that came up during a meeting of the Awareness project advisory board two weeks ago at Edinburgh Napier University. Awareness is a project bringing together researchers and projects interested in self-awareness in autonomic systems. In philosophy and psychology self-awareness refers to the ability of an animal to recognise itself as an individual, separate from other individuals and the environment. Self-awareness in humans is, arguably, synonymous with sentience. A few other animals, notably elephants, dolphins and some apes appear to demonstrate self-awareness. I think far more species may well experience self-awareness – but in ways that are impossible for us to discern.

In artificial systems it seems we need a new and broader definition of self-awareness – but what that definition is remains an open question. Defining artificial self-awareness as self-recognition assumes a very high level of cognition, equivalent to sentience perhaps. But we have no idea how to build sentient systems, which suggests we should not set the bar so high. And lower levels of self-awareness may be hugely useful* and interesting – as well as more achievable in the near-term.

Let’s start by thinking about what a minimally self-aware system would be like. Think of a robot able to monitor its own battery level. One could argue that, technically, that robot has some minimal self-awareness, but I think that to qualify as ‘self-aware’ the robot would also need some mechanism to react appropriately when its battery level falls below a certain level. In other words, a behaviour linked to its internal self-sensing. It could be as simple as switching on a battery-low warning LED, or as complex as suspending its current activity to go and find a battery charging station.

So this suggests a definition for minimal self-awareness:

A self-aware system is one that can monitor some internal property and react, with an appropriate behaviour, when that property changes.

So how would we measure this kind of self-awareness? Well if we know the internal mechanism because we designed it), then it’s trivial to declare the system as (minimally) self-aware. But what if we don’t? Then we have to observe the system’s behaviour and deduce that it must be self-aware; it must be reasonably safe to assume an animal visits the watering hole to drink because of some internal sensing of ‘thirst’.

But it seems to me that we cannot invent some universal test for self-awareness that encompasses all self-aware systems, from the minimal to the sentient; a kind of universal mirror test. Of course the mirror test is itself unsatisfactory. For a start it only works for animals (or robots) with vision and – in the case of animals – with a reasonably unambiguous behavioural response that suggests “it’s me!” recognition.

And it would be trivially easy to equip a robot with a camera and image processing software that compares the camera image with a (mirror) image of itself, then lights an LED, or makes a sound (or something) to indicate “that’s me!” if there’s a match. Put the robot in front of a mirror and the robot will signal “that’s me!”. Does that make the robot self-aware? This thought experiment shows why we should be sceptical about claims of robots that pass the mirror test (although some work in this direction is certainly interesting). It also demonstrates that, just as in the minimally self-aware robot case, we need to examine the internal mechanisms.

So where does this leave us? It seems to me that self-awareness is, like intelligence, not one thing that animals or robots have more or less of. And it follows, again like intelligence, there cannot be one test for self-awareness, either at the minimal or the sentient ends of the self-awareness spectrum.


Related posts:
Machine Intelligence: fake or real?
How Intelligent are Intelligent Robots?
Could a robot have feelings?

* In the comments below Andrey Pozhogin asks the question:What are the benefits of being a self-aware robot? Will it do its job better for selfish reasons?

A minimal level of self-awareness, illustrated by my example of a robot able to sense its own battery level and stop what it’s doing to go and find a recharging station when the battery level drops below a certain level, has obvious utility. But what about higher levels of self-awareness? A robot that is able to sense that parts of itself are failing and either adapt its behaviour to compensate, or fail safely is clearly a robot we’re likely to trust more than a robot with no such internal fault detection. In short, its a safer robot because of this self-awareness.

But these robots, able to respond appropriately to internal changes (to battery level, or faults) are still essentially reactive. A higher level of artificial self-awareness can be achieved by providing a robot with an internal model of itself. Having an internal model (which mirrors the status of the real robot as self-sensed, i.e. it’s a continuously updating self-model) allows a level of predictive control. By running its self-model inside a simulation of its environment the robot can then try out different actions and test the likely outcomes of alternative actions. (As an aside, this robot would be a Popperian creature of Dennett’s Tower of Generate and Test – see my blog post here.) By assessing the outcomes of each possible action for its safety the robot would be able to choose the action most likely to be the safest. A self-model represents, I think, a higher level of self-awareness with significant potential for greater safety and trustworthiness in autonomous robots.

To answer the 2nd part of Andrey’s question, the robot would do its job better, not for selfish reasons – but for self-aware reasons.
(postscript added 4 July 2012)