In this episode, Audrow Nash interviews Todd Hylton, Senior Vice President at Brain Corporation, about neuromorphic computers. They discuss the robotics development board bStem, which approximates a neuromorphic computer, as well as the eyeRover: a small balancing robot that demonstrates how the bStem can be used in mobile robots.

youtu.be/uGcUB5MAVsY

Todd Hylton 

As Senior Vice President of Brain Corporation, Dr. Todd Hylton leads the development of business and technical strategies within the company. A scientist and co founder of a small semi-conductor equipment manufacturer, Hylton brings 25 years of experience in the semiconductor, optical communications, data storage and defense industries alongside a broad technical entrepreneurial background in research and development, small business, marketing and government programs.

Links:

• Download mp3 (16.4 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

Transcript

Audrow Nash: Hi, welcome to robot’s podcast. Can you introduce yourself?

Todd Hylton: I’m the senior VP of Strategy at Brain Corporation. Brain Corporation is a technology startup located in San Diego, California working on computing hardware and software technology for robotics.

Audrow Nash: Can you tell me the goal and motivation behind the company?

Todd Hylton: The company’s mission is to provide computing technology – hardware, software and cloud-based or web-based services – for people who want to build robots, so that robots can become more a part of everyday life than they currently are.

I often ask people, “How many robots did you see today?” And usually the answer is “none” unless they have a [robot] vacuum cleaner at home. Our mission is to make it possible for many more robots to be created by providing some of the foundational technologies that are, frankly, quite challenging to produce if you’re building a robot from scratch.

Audrow Nash: To begin, can you give me an overview of neuromorphic computing and its advantages over using a CPU?

Todd Hylton: This is a topic near and dear to my heart, and certainly part of what Brain Corporation does.

Before working at Brain Corporation, I was at DARPA where I sponsored a project in neuromorphic computing called SyNAPSE. The goal for neuromorphic computing in general was to see if it was possible to build a different kind of computing architecture, one in which computing was massively distributed, and memory was embedded very near the computing elements. To draw inspiration from biology: very simple processing units (which you might think of as neurons) sending messages to other processing units (via things you might think of as spikes) with connections between them, which are the memory around the processors (what you might that of as synapses).

The goal for that project was to see if it was possible to build a large-scale neuromorphic computer and also to push boundaries in memory technology. You need a lot of memory on board to make a good neuromorphic computer, and [we wanted] to push our understanding of what we would be able to teach these chips to do, and how we would teach them by having synaptic modifications and neuronal dynamics built into the chips.

Though Brain Corportation is not building neuromorphic chips, many of the learning algorithms that we write for our robots have ideas like neurons and synapses underneath them, so we would very much like to be able to buy these neuromorphic chips when they become available because we think they will be a great enabler for our robotics packages.

To come back to your original question about CPU versus neuromorphic processor, CPU’s are great for general purpose computing. You can give them gigantic lists of instructions, they’re pretty simple to use, and that’s why we like them. But if you need to do a whole bunch of things in parallel – if you need to coordinate many different things in time and space – they’re not very efficient. It’s just too much energy wasted moving stuff back and forth in the chip.

Audrow Nash: An example of that is vision processing … ?

Todd Hylton: Yes, a good example of that is vision processing, which goes right back to robotics. One of the key things that robots need that they mostly don’t have are vision systems. There are different levels of vision system, and some are quite simple, but we envision at some point in the not too distant future a vision system that actually begins to learn the dynamics and the statistic of its environment in a spontaneous way. And that really needs a different kind of computer to do it well.

Audrow Nash: You guys have created bStem. Can you tell me a bit about it?

Todd Hylton: bStem does not include a neuromorphic processor – those neuromorphic processors are still in their very early phases of being built and the tools needed to make them are not commercially available yet. But bStem is the closest thing we can get right now.

We wanted a processor that was low power, low cost, but still with a lot of computational capacity, because you don’t have a lot of power and you need a lot of computation if you’ve got a mobile robot. bStem’s basic technology is derived from the mobile phone industry. Chips that are available for mobile phones provide lots of different computing capabilities: GPUs, CPUs, DSPs, various codecs for vision – all of these things are part of what we need. So we have taken state-of-the-art cell phone chipsets made by Qualcomm and repurposed them for robotics on this little board we called bStem. It also comes with sensors, drivers for various motors, and breakout boards for various motors that the robots may have.

Audrow Nash: Are there similar development boards out there?

Todd Hylton: I don’t know of any similar development boards that use state-of-the-art mobile phone technology for robotics. But there certainly are development boards that use mobile phone technology. If you want to build a phone or tablet you can get one of these boards. Unfortunately they’re a long way from what a roboticist really wants. With bStem, we closed the gap between those development boards built for phones and development boards built for robots. If get a bStem, it’s going to look and feel just like what you would expect it to be as a roboticist and not as a mobile phone developer.

Audrow Nash: What kind of reactions have you gotten from people who are using bStem?

Todd Hylton: We’ve given bStem to a handful of developers. The basic reaction is, “Wow I can’t believe it. I’ve got a whole Ubuntu package on this tiny little board, it takes about two watts to run it, I can develop my whole robotic controller on this board, I can do Gmail on it if I feel like it, plus it’s got a whole bunch of other tools and goodies that make it easy for me to write the controllers that I want.”

Audrow Nash: Can you tell me a bit about eyeRover?

Todd Hylton: As I said in the beginning we aspire to build the computing technology for people that want to build robots. We don’t plan to build and sell robots, but we built the eyeRover because we had to integrate the whole technology stack: the computer hardware, the software, put it on a robot, put the sensors on it and we wanted to be able to train the robots so it’s got a learning system that we put on it as well. It basically forced us to build the whole technology piece so that we could begin to show our potential partners what it is we can do.

Audrow Nash: Can you describe some of the sensors and actuators that eyeRover has?

Todd Hylton: eyeRover is a two-wheel balancing robot, sort of a Segway-type robot. It’s got two cameras and a relatively simple vision system with which it can see its environment. It has a bStem board, onboard IMUs, and magnetometers (which are used in some of the algorithms).

There are a lot of things on bStem that it doesn’t use because we didn’t need them for this particular robot, but other roboticists would probably like them. bStem also includes a user interface, which you can use to both remote control the robot and to train the robot in a behavior. That remote controller looks like a game pad/iPad combination. It’s sort of like a video game controller for the robot.

Audrow Nash: It looks to me like a PSP, the older kind.

Todd Hylton: Yeah, yeah.

Audrow Nash: Can you talk about eyeRover’s modes of learning and how you’ve been able to train him?

Todd Hylton: All of the learning modes on eyeRover are what we call ‘supervised learning’. Supervised learning is when you essentially show the robot what it is supposed to do; it learns to associate its sensory input with whatever it is you told it to do. For example, if the robot is supposed to turn right whenever it sees a green block on the left, I just show it that behavior a few times and it makes that association. It’s a well-known way of doing training.

We’ve built many different supervised learning algorithms, and the ones that we do on the eyeRover we’re doing primarily because we wanted to show navigation behaviors; we basically built our own special supervised algorithm that we thought was particularly effective and also didn’t require gigantic computational resources.

In the future, as we build out the learning piece – which we call the Brain Operating System, that’s the software stack that allows the robot to learn – it will also have what people generally think of as ‘unsupervised learning,’ where by exploring its environment the robot begins to learn the structure of its world. It will likely have some reinforcement learning as well, which is what I kind of think of as an impoverished supervised learning where instead of telling the robot exactly what it’s supposed to do, you just tell it “that was good” or “that was bad.”

Audrow Nash: Can you talk about some of your examples of actually training eyeRover?

Todd Hylton: We’ve trained it to do simple navigation around objects that may be on the floor, like around your desk, around your chair, around your trash can, do some loops and figure eights, go from point A to Point B. The way we train it is to show this behavior by remote control a few times, we show it a few loops around the path it’s supposed to do, and then we let it go. And if it’s correctly learned it, it does the same behavior. You can also train it incrementally, where you train it, you let it go, and you see if it’s doing what you want and you just give small corrections when it makes mistakes. Over time it does better at those areas where it was making mistakes. For the kinds of things that we’re doing now, it usually only takes a couple of minutes to train a path like the ones I just described.

We’ve also given it some simple gestures and it can learn to come to you or go away from you. On other robots we’ve trained them to play fetch and we can show them an object that they’re supposed to find: put the object out somewhere, it’ll go get the object and bring it back to a base, and it’s all trained.

The cool thing about that is it’s hard to write a piece of code that would just do that generically. You can’t foresee every environment even if they’re pretty simple, and you can’t really code it. So one of the key things we think robots need to be able to do to be part of everyday life is that they need to be able to adapt or be trained in the environments in which they’re going to perform because no coder can anticipate every single situation a robot’s going to see.

Audrow Nash: Will this bring the development of robots and their behaviors to the masses … because you can train the robot to behave as you want?

Todd Hylton: Exactly.

Audrow Nash: Is that the goal of Brain Corp?

Todd Hylton: That’s one of the goals, and I think it’ll go in stages. There will be some pre-training by the people who build the robots so that the thing doesn’t have to learn from scratch when you buy it from Best Buy and stick in your home. That way, when you get it in the home, it will be much more capable and much more useful, [especially] if you can give it additional training.

One of the challenges for us, though, is to come up with user interfaces so that people who aren’t roboticists or technologists can train a robot. There’s a big stack of things that we worry about, such as what can people actually do, how can we actually control the robot, how can we make it clear, how do you communicate the task to the robot … the AI pieces that try to put together what the robot senses and what it’s being told to do … all the computing hardware and then a whole cloud infrastructure so that you can get updates, you can upload brains, you can upload what the robot’s done so we can diagnose it if something bad has happened and it’s not performing the way it should be. We are doing all of those things.

Audrow Nash: Now that you’re using bStem, what are some of the other applications that you anticipate seeing from the user community?

Todd Hylton: That’s what we’re just now beginning to learn in detail. We were in hibernation for many years getting the technology together, and it’s only in the last month or so we’ve actually been telling people. We didn’t really want to start saying much until we had a platform on which we can demonstrate it, which is why the eyeRover exists. 

What I see in the robotics industry in general, like at this show – we’re at RoboBusiness now –it’s mostly very small companies in narrow niches. [Of course this is a] silly exaggeration, but it seems that most of the companies bring in gears and motors and sheet metal and plastic and a few chips and a loading dock, and a robot comes out the other end. Basically, they have to do the whole technology piece, which is really challenging.

What we’re doing took a gigantic amount of resources to actually accomplish. Our business proposition is that it doesn’t make sense for every company to do that; we’ll do it for you. Pretty much all the robots that I’ve seen at this meeting are simple robots that roll around on four wheels and see stuff, there are some that have grippers, there are some humanoid robots or some combinations of those things. I think all of those robots could benefit from the technologies that we’re developing.

Another thing that we would like to see happen in the robotics industry is for it to be much easier for new companies to form around building robots. There’s a huge economic barrier to get it going because the computing systems that you need mostly don’t exist and you have to build them from scratch. The people that really know robotics or who have some deep domain expertise in whatever the problem is they’re trying to solve – whether it’s drilling holes or sweeping floors or cleaning windows or whatever. There’s no reason they should have to know the entire software stack on a modern system on a chip – it’s crazy.

If we can get these technology pieces together and available – and that’s our plan of course – then I think there will be an explosion of new robotics companies. Two guys in a garage out of engineering school can build a robot, rapidly prototype it, show that it works, take it to an investor and validate that they can do what they say they want to do with very little investment. That will enable an explosion of robotic applications and different niches, and that’s the world as we see it in the future. Sort of what we have now, except it would be vastly larger.

And of course there will some big consumer plays where everybody’s got the robot that cleans the table, or whatever the killer application is, and we’re still talking to people about that.

Audrow Nash: In developing this technology, something like neuromorphic computing, what are some of the major challenges you’ve encountered and what is the lesson to learn from them?

Todd Hylton: The major challenges now and for the foreseeable future will be getting sufficient computational capacity on board small mobile robots. One of the ways we are working to mitigate is to try to shift part of the computation off into the cloud. That turns out to be a very complicated problem, too. It’s not as simple as saying, “There’s big computers in the cloud and we’ll just do it all there,” because there are all sorts of issues about moving data around and latency that it may make sense for some applications but not for others.

From my perspective, the biggest technical challenge is the computing capacity. If the neuromorphic computing technology matures rapidly, it would alleviate a great fraction of it. Most of the computation that we do now, even on eyeRover, is vision – vision algorithms of kind or another – and that’s a real sweet spot for neuromorphic computers.

I think the other challenge for us is that the industry structure needs to change, and I’m not sure how long that will take. As a business, you’ve got only so much capital to work with, and ou’ve got to become self-sustaining and profitable before you run out of money. That depends on the ability of the industry to adopt what we’re doing even if they maybe don’t think about it the way we do yet. So that’s a certainly a risk for us.

In terms of lessons learned, I would say it’s really challenging to take a state-of-the-art system on a chip built for a mobile phone and make it useful on a robotics platform. When we first started, we thought it was going to be a whole lot easier than it was. A lot of people talk about how mobile phone technology is going to enable robotics, and it will, but actually getting there from where we are now was a huge investment.

An additional lesson that we learned is in doing the learning algorithms; that there’s not one way to do them, there are many ways. There is no general-purpose learning algorithm out there – at least not yet. So you have to do some experimentation to figure out what the best ones are. I like to tell people that the work is the process of eliminating all the bad complicated ideas in favor of the simple ones that work. Sometimes, though, when you find a simple solution you kick yourself and wonder, “Why didn’t I think of that a long time ago?” but that’s just the way it is. The goal is to find a simple solution, and that takes a lot of work. Brain Corporation’s got some very capable people working for it, but we’re just a small group of people. There are all kinds of people who can write these kinds of algorithms, and there’s no reason we shouldn’t make it possible for them to do that.

So part of our strategy going forward, hopefully sometime next year, is to make our Brain OS system available with APIs so that if you are into AI, or a neural nets, or machine learning, you could start writing your own learning algorithms. You already have the whole robotic platform, all the infrastructure and so forth, and you can focus on that piece, because that’s a lot of work too. If I can multiplex that out into the world then I think there will be a much faster spread of the technology, and I think it’s a much better business strategy for us as well.

I think the other lesson I’ve learned is that it’s very challenging to try to build a new technology and build a new business model and into a new market at the same time. It forces us to make some educated guesses when it’s still quite unknown what the result will be. It takes a real leap of faith to say, “We’re going to build an eyeRover,” or “We’re going to spend a year building this little plastic robot that runs around so you can train it,” because if I don’t have it, I can’t show anybody what I’ve got. And then you say, “What if the eyeRover is the wrong sort of thing? What if nobody likes it?” or “What’s the application?” and so forth. You have a constant chicken and egg problem, but that’s also what makes it so exciting.

I have all these great people that want to work the hard technology problems, the business problems, and fortunately there’s an appetite for it now, so investors are interested and that enables us to do it.

Audrow Nash: For those that have less expertise, what would be some ways that they can get involved, learn more and eventually contribute to this technology?

Todd Hylton: Currently the eyeRover is in a closed beta program – we’ve just got a handful of people using it so we can get feedback on what we’ve got and shake out the bugs that we’ve missed. But sometime next year it’ll be possible to buy that computing system from us, the boards and the software and so forth. The eyeRover is mostly 3D printed, so we’ll just make those files and the parts list available, and you can buy the board and build your own. We’ll probably put a couple more robotic designs that are 3D printed like that. If people are enthusiastic about it and have access to some resources like a 3D printer (and of course you order 3D printed parts online), then that’s one easy way to get going. You still need some degree of expertise in computers and to use the board and change things on it. But it’s a Linux operating system; you basically turn it on, you plug the robot into your monitor and keyboard, and the screens come up. It looks just like your desktop, so it’s not nearly as intimidating as it used to be.

Audrow Nash: Fpr Brain Corp, what is your future direction and some of your future goals?

Todd Hylton: It’s pretty clear on the technical side what we’re doing for the next year, and it’s pretty ambitious. We’ve got lots of developments going on and new hardware, new learning algorithms, APIs for developers. On the business side we are just breaking out box, so we are talking to a lot of people, and we need to refine our business model. We were a technology vendor essentially, but there are different ways to do that. How do you capture it in such a way that you provide the best value to the customer, but you also make money on it? In order to do what we really want, we need to be someday a really big tech company. So we’ve got to figure out a business model that makes it possible for that to happen.

Audrow Nash: Wrapping up, what do you think is the future of robotics?

Todd Hylton: Robotics is always the technology that’s just around the corner and never quite gets here. That says to me that it’s hard. You can ask yourself whether now is the time or not, but there are certain things that suggest that maybe now is the time, and of course if I didn’t believe it I wouldn’t be doing what I’m doing. On the one hand there is the explosion in low cost computing hardware – it’s gotten really cheap thanks to Moore’s Law. And that’s going to continue for a while so we’re going to get better and cheaper computers, and maybe the neuromorphic stuff will come along, too, and that will be a big tech enabler.

There has been a huge amount of work in neural nets, AI, and machine learning over the past three decades. It really hasn’t had much of an impact on robotics yet, but a lot of people know how to do that now. There are a lot of tools out there, and people are trained in it so you can hire them. That’s a definite tech enabler for robotics, too, that in the past hasn’t been as prominent as it is now.

On the economics side are the big tech companies, the Intels and Qualcomms and Googles of the world. They’re looking for the next big thing and they can’t avoid robotics. Everybody says we’ve got to have robots to take care of old people, and I will be one soon enough. And it’s true. We really do need them. There is going to be a huge demand if we can get the technology and the industry structured appropriately in time. That’s why I’m bullish on it and I think it is a good time for robotics. Another important thing is that we’ve done a lot of work studying the brain lately, and although we are never going to put a mouse brain on a robot, knowing something about how brains work is really important for giving us inspiration for how to do it in a different kind of technology, like a computing technology. So that’s also why I think it’s a great time.

Audrow Nash: Thank you.

Todd Hylton: Thank you.

All audio interviews are transcribed and edited for clarity with great care, however, we cannot assume responsibility for their accuracy.

Dmitry Grishin at the Skolkovo Robotics conference in Russia.

As part of our series on high-risk, high-reward robotics, and as a follow-up to his recent Robot’s Podcast interview, I asked entrepreneur and venture capitalist Dmitry Grishin to talk to us about how he manages risk in his global investment company Grishin Robotics. 

[HS]
How would you define high-risk, high-reward robotics from an investor’s perspective?

[DG]
I don’t look at companies in terms of ‘this is high risk’ and ‘this is low risk’. I have a different approach: I try to find a company where I believe in what they’re doing, where I believe in the founders, and I really believe that this company can be a great success.

I like to look at it from a pragmatic perspective, so first I want to know what kind of practical problem the company wants to solve. Second, I want to understand the market, because inside robotics you have a lot of smaller markets … telepresence, home automation, security, etc. So I want to know how big this market is. And of course, very critical for me is the team: how it’s organized, what’s the background of the founders, and how they work together.

You say that teams are really important. How much does a founder’s personality or reputation factor into how you perceive the investment risk?

In my experience founding an Internet company, I learned that you definitely have to have a person who understands technology: it’s very critical that one of the founders understands how to build robots and how the technology works. But I find that the best combination is to have two people: one who is more technical, and another who is more of a strategic marketing, sales and product kind of person.

It’s rare to find these two kinds of skills in one person. A lot of the time, when technology people start companies, they’re very excited about the technology but they pretty much don’t know what to do with it. They say, ‘We have some super cool technology,’ but when you ask, ‘How do you want to build your business? What’s your plan?’ they don’t know the answer. Unfortunately this problem is very common.

If people are missing some of these skills, do you see it as your role to provide some aspect of that in terms of mentorship? Or do you expect the companies that you invest in to already have those skills in place?

I try to find the companies that already have those skills in place, or that at least have the potential to go there, because I think these skills are critical. If within the company you don’t already have product DNA or a good understanding of the customer, for example, it’s very difficult to create this culture from the outside.

I can definitely help with experience and help explain the market, but I don’t want to be the leading guy. I don’t want to tell people what to do. I’m available to entrepreneurs to answer their questions and give some advice, but it should be their decision in the end. It’s only the way you can build a good company. I think that if an investor starts to manage the company, it’s just a road to nowhere.

So essentially what you’re saying is that, even if the market is perhaps more risky, you might be willing to invest in a company as long as the right team is in place, if they’ve got all the skill sets, and the right product to solve a problem. Is that correct?     

Yes, but it’s also important that I personally believe in this market. For example, in the early days of the Internet, nobody understood exactly how it worked, or what businesses there would be around it, but there was a group of people who really believed that the Internet would one day be a big business and make a big impact.

The same is for me. I really believe in robotics in general, and particularly in several markets inside of robotics. Of course there are still lots of questions in terms of how exactly it will work, and what kinds of business models will be successful, but I definitely think the robotics market will be very big – and what’s more interesting – that it will solve a lot of problems for human beings.

What are the robotics markets that you really believe in right now?

I really do believe in telepresence, but of course this market is in a very early stage, and the companies involved here need to prove that they can find the right products and the right audience for these products. 

Would you say that some markets in robotics are more risky than others at the moment?

Yes absolutely. There are definitely already some more established markets, like vacuum cleaners, for example … the market is already there and everybody understands that you can sell a vacuum cleaner to a customer. But of course you already have players there: iRobot, Samsung and LG entering this market, and a lot of Chinese companies as well.

On the other side, you have something like telepresence. We know that police and military organizations are already buying a lot of this, so it’s wanted, there’s a market. But it’s less predictable in the consumer market because it’s at such an early stage.

You tend to invest in more mature consumer robotics and not so much in moonshots. Is this because there’s a lack of well-considered moonshot proposals out there to choose from, or are you just not interested in those kinds of projects?

First of all, I think mature is not the right word for the companies that I’m investing in. I think of mature as a public company that has a big valuation. If you ask me whether I’m going to invest in a very early stage, the answer is no. I want to invest in a company that already has some kind of prototype … If you want to try to start something, you really need to invest at least a small amount of money yourself, or have some kind of support from your friends or family, or from a source like Kickstarter … because if a team is successful at building some kind of prototype and is able to get some sales from it, it’s a sign that the team has the experience to take a concept to a first project, and this is an important criteria for me.

Can you tell us about any particular robotics technologies that you would classify as being especially visionary or innovative? 

I think there are two kinds of innovation: one where you create something absolutely unusual, and another where a product already exists but you figure out how to bring it to the level where millions of people can use it. An example of this second kind of innovation is the iPhone: touch screens existed before, smart phones existed before, but Apple created a product that millions of people started to use, and this kind of innovation is no less important than the innovation of inventing something new.

But if you’re asking about absolutely new directions that have very high risk and yet are potentially very promising, I see the space market as one very interesting area. Satellites, for example, are getting very cheap to make and though they are very high risk, I find them very promising. I also think that anything related to robotic cars is a very interesting market for investing, and yet very high risk because there still needs to be significant innovation to bring these technologies to the market. Drones can also be in this category.

Is resistance to adopt these technologies on the part of the consumers a part of what makes them risky?

I think there’s a lot of resistance, and infrastructure and people’s minds change slowly at first. But once some level of change has been achieved, adaptation happens very quickly. If you look at the first cars or railroads, we initially saw very slow adaptation, but then the adaptation accelerated.

My belief is that robotic cars will repeat pretty much the same route as hybrid cars, where we spent ten years using electric and gas cars together before there was some kind of visible market share. And this is not just an example, it’s a model we should be using: anytime we are thinking about a robotic product that involves a lot of people and infrastructure, we should be thinking about hybrid models. Mercedes Benz, for example, introduced a car that can park itself, but you still need a person to drive the car. I think the two technologies – self-driving cars and cars driven by people – will live together for quite some period of time, maybe ten or twenty years before they merge together.

In the Robots Podcast interview from last November you said that a lot of people have good ideas, but the time isn’t always right for investment. Is there a right time to invest in higher risk initiatives? Is now a good time?

Definitely right now is the right time for the robotics market in general, and I really believe that the biggest innovations will be done first by startups. With robotics, it’s not just software, it’s also hardware, and ten or twenty years ago it was almost impossible for a small startup company in robotics to get going because it was too expensive. But now electronic components for robots are really cheap, and 3D printers help a lot with prototypes … so you can have a team of three to five engineers working on a product, and then they can change it based on feedback from users in a test market. The whole process of product development is accelerated significantly.

Do you have a message for young entrepreneurs and students out there who are getting ready to enter the robotics market?  

Right now you have an opportunity that didn’t exist ten or twenty years ago. Right now you can start a robotics company, and the market you create will be able to change people’s lives.

I want you to think about this: if you have a good idea and a good team, come to us and we’ll try to help. Just focus on what kind of problem you are solving – not the technology – and think about your team very deeply, because the people with whom you co-found will be very important for your future success.

Based on a media rountable discussion with DRC Program Manager Gill Pratt and CEO of the Open Source Robotics Foundation Brian Gerkey.

The Fukushima Daiichi Nuclear disaster was a wake-up call to the robotics community. In Japan, many asked why a country known for its cutting-edge robotics sector was unable to respond to the emergency. Worldwide, robotics experts pointed to the event as a real-world test of what robots can and cannot do.

Whether man-made or natural — or like Fukushima, a combination of the two — major catastrophic events, while rare, are becoming increasingly costly as human populations worldwide move to urban areas. This is why, in an effort to spur the development of agile humanoid first-responders, the US Department of Defense’s strategic plan identifies disaster response as a priority area, and why it is funnelling tens of millions of dollars into the DARPA Robotics Challenge.

DRC Program Manager Dr. Gill Pratt envisions a future where tele-operated robots can be quickly deployed by disaster-response personnel with minimal robotics training: “In Fukushima, during the first 24 hours, the explosions would not have occurred if it would have been possible to vent the reactors,” said Pratt in a media round-table last month. “Human beings tried to do this but had to turn around [because of the radiation]. Our hope is to develop machines that can actually intervene and help make a disaster [like this one] less severe.” Estimates of the total economic loss due to the Fukushima disaster range from $250B – $500B US.

Why a humanoid?
Though having a robot that can safely navigate dangerous places like the inside of a nuclear plant is no-doubt useful, one has to question whether the extreme cost and design challenge of developing a humanoid that is capable of doing so is worth the effort. Yet as Pratt points out, a disaster scenario that is catastrophic to humans is also most likely to occur in an environment that has been engineered for humans, and what kind of robot would be more capable of navigating in a human environment and using the tools found there than a humanoid?

As DARPA project leaders were designing parameters for the Robotics Challenge, they consulted with first-response experts — including the Nuclear Regulatory Commission — and identified key capability requirements that indeed seem to point to a humanoid solution: the ability to go through doors, climb steep stairs, walk on rough terrain, and the ability to use whatever tools are oh hand, such as a utility vehicle or a screw driver.

“Our belief is that we will never know where the next disaster is going to hit and when it will be, and so we have to be prepared to use whatever tools are on hand,” said Pratt. “And at least for the foreseeable future, those are tools for people to use, and so we need to adapt our machines to use those tools as quickly as possible.”

Designing a roadmap for success
DARPA does not define the success of its programs through a profit and loss model, and its goal with the DARPA Robotics Challenge is not to bring a humanoid first-responder to every fire station in the country. As a government agency, DARPA focuses on bringing high-risk technologies to the point of prototype, and then handing these off to the military or commercial sector for development. It invests in challenges such as the DRC in order to spur radical innovation, and short-term technical failure is OK as long as the long-term potential payoff is high.

But despite these lofty goals, the question remains: what’s the roadmap for designing and overseeing a major robotics challenge such as this one? How does one gauge success when the potential payoff may be decades or more away?

DARPA’s previous autonomous car challenges offer some lessons, and one of them is that there isn’t always a winner.  Pratt admits that success for the DRC isn’t guaranteed, but says that the goal of the competition isn’t so much to declare a winner as it is to continually nudge the competitors towards greater achievements, while at the same time being realistic about what is actually doable. As a result, DARPA will be reevaluating the project’s milestones and adjusting the difficulty of the tasks in consultation with the participants along the way.

Previous autonomous car competitions are different from the current challenge in one major aspect, however: the older competitions were based on automotive technology, which already had a significant infrastructure in place. The robotics industry, by comparison, is still in its infancy, and so a great many hurdles exist for competitors, both at the software level and at the hardware level, and in terms of integration. Pratt has thus broken the competition down into three stages and four separate competitor tracks in an attempt to structure what is otherwise an extremely open-ended problem. (You can find a good explanation of how the competition breaks down on DARPA Robotics Challenge Fact Sheet.)

The first stage of the DRC — the Virtual Robotics Challenge (VRC) — offers particular insight into DARPA’s strategy for success. The VRC, for which the results were announced earlier this month, was a cloud-based competition designed to test software, and its goal was to evaluate the teams’ robot perception, manipulation and locomotion in a virtual environment as a first step before moving on to the physical trials, which will take place in December of 2013. The big idea here is that teams will be able to try out their strategies in simulation first, before investing in hardware. “The reason we had this virtual challenge is that we wanted to open the contest to teams whose strength is primarily software,” said Pratt.

DARPA’s simulator models real-world physics and behaviours with incredibly high accuracy. Brian Gerkey, CEO of the Open Source Robotics Foundation (OSFR), the organization behind the VRC simulator, says that their goal is for the simulation to be the best possible stand-in for the real robot, so that teams that have done well in the VRC should be able to run their software virtually unchanged on the physical robot during the trials. And yet run to such a complex simulation — and to play that simulation back at interactive rates — takes a huge amount of computer power.

“One of the key developments over the past several years has been the advancement of cloud computing resources,” explained Gerkey. “It’s now possible to rent access to cloud computing resources so that you can have modest computer at your end and have the simulation run somewhere else. That’s a key aspect of this project and it’s really required in order to run the competition.”

The seven winning teams from the VRC challenge each received an Atlas furnished by Boston Dynamics for use in the DRC trials, where they will compete with other funded and unfunded teams that have been developing their own hardware (see DARPA’s Meet the Teams video to get a sense of what Atlas will be up against). They now have until late December to transfer their software algorithms to the Atlas hardware shell, and practice running a real machine in real environment. The physical trials should be a good demonstrator of the success of the simulator approach to competition design.

Building a legacy
DARPA takes a long view to technology development, starting with identifying a major national security need (in this case: disaster response), then determining the specific technologies and innovations needed to address that need (in this case: easily deployed agile humanoid robots), and finally working to support the fundamental breakthroughs required to make these technologies possible. DARPA’s disaster challenge is thus about creating infrastructure to support long-term software and hardware development in the robotics community; it’s also about attracting bright minds to solve some of the world’s most pressing problems.

One of the legacies of the VRC is that all the software code for the simulator is being distributed open source, and so, as Gerkey points out, there is no cost to reuse it later.

“The history of software development has shown that this is the best possible, most efficient development model for building the common platform that a whole community comes to rely upon,” says Gerkey. “The simulation work that we’re doing is really part of a broader effort to build a common ecosystem of software tools and libraries that everyone can use. There are computers that are powerful enough to run this simulation that you can rent for on the order of a couple of dollars an hour.”

The hope is that, ultimately, the open-source VRC simulator will help speed up the development of robots in general by lowering the barrier of entry and that this, in turn, will lead to more capable robots – perhaps ones capable enough to be of service in a Fukushima-like disaster. Says Gerkey: “Once we can run this very realistic simulation in a competition, we can then try this in a classroom scenario. You don’t have to outfit your classroom with expensive powerful computers.”

Pratt agrees: “Right now we have a few really talented designers and a few companies that are beginning to be strong in the robotics field. We would like to see those numbers go way up, and we believe that the way to do it is to give people a tool to develop these machines without having to incur the cost of first building them.”

Fostering international cooperation
Fukushima is used as an example to motivate the need for robotic platforms in dangerous environments, and the VRC is being promoted as an internationally beneficial endeavour, so I asked Pratt what specifically is being done to foster international cooperation, and whether there was a possibility that DARPA would work with other countries on future challenges.

Pratt answered that a key measure for success in this project is engagement with the international community. “All the code that we’re writing is distributed under the Apache 2 licence which is a very permissive open source license that allows anyone in the world to do anything that they want with this software. We would love for the challenge (once we are done with it in the next year and a half) to move around the world, and we have been in discussions with our counterparts in Japan and the EU about the possibility of this becoming a rotating contest. We think that [making societies more resilient to disasters] is a need that the whole world has, and so and I’m spending quite a lot of my time having lots of discussions about what the future of this is going to be once we are done … I personally think that the international part of this is a key way to measure how well we have done, so if we’re successful in having the DRC continue on somewhere else in the world, then it will be clear that we have done well.”

Though Pratt hesitates to predict how long it will be before we have robots capable of helping out in situations such as the one in Fukushima, he is convinced that DARPA’s Robotic Challenge will result in some major developments for the field:

***

More info:

DARPA Robotics Challenge Website
DARPA Robotics Challenge Fact Sheet

Look out for DRC trial coverage in December!

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)

As part of our series on ‘High-Risk / High-Reward’ robotics, I interviewed SRI International‘s Director of Robotics, Rich Mahoney, who’s role there is to help identify important emerging robotics technologies, align them with the needs of funding sources, and bring them successfully to market.

[Hallie Siegel]
Tell us about yourself, SRI, and your role in the R&D and entrepreneurial space…

[Rich Mahoney]
I’ve been at SRI for five years, and my position before coming to SRI was in an early stage venture-backed environment as the general manager of a company introducing and developing a new robot for stroke therapy in the US market. Before that, I did product and business development, and before that I did university-based research just after finishing my PhD. So, I have a broad background in applying robotics, from university research to commercial product development to venture-backed early-stage companies.

What’s interesting is that, because of SRI’s business model as a nonprofit, I actually practice my background in all of those areas in just one job. SRI’s primary business is contract research and development, but our overall mission is to invent, apply and commercialize technologies. As the director of the robotics program, I’m responsible for running the business of our robotics program. I have profit and loss responsibility for our performance in this nonprofit environment where we’re really working towards a higher mission of impactful R&D.

It’s a very aggressive and difficult environment, and it’s for people who want to work in a place where they can have a lot of impact: where they can work on applied solutions, and where the problem-solution is the focus, rather than the PI-driven research that you see at universities.

SRI is an offshoot of Stanford, correct?

You can say it’s a spin off from Stanford University. SRI originally stood for Stanford Research Institute, when it was founded 65 years ago, but it spun out as an independent nonprofit in the 1970s. It was doing some military-oriented research at that time – during the Vietnam War era – and the students didn’t want that kind of work going on on campus. Instead of closing it down it was spun off and made independent. Since then it’s really developed its own identity, and now we’ve got 40 different programs working on leading edge research. It’s not all hard-core technology like robotics; some of it is education policy…  some of it is pharmaceutical discovery …  so we operate in a very broad base of areas, but always with the mandate to work at the leading edge of applied solutions.

It seems like SRI operates half way between a university innovation lab and an investment company…  

That’s right: there’s university research, and then there’s commercial research, and SRI lives right between those two… in a space often referred to as ‘the valley of death’ [Chuckles.] It’s where things go that get invented, but which just can’t seem to get applied and transitioned to the commercial world. It’s very dry to say our primary business is in R&D: I would say that SRI is a big party tent in the valley of death! [More laughter.] People at SRI don’t just work there, we thrive there: we’re just really good at taking early stage technologies and transitioning them into practical value.

As we were developing this series it became clear that “high risk, high reward” can mean different things to different people … the researchers on our team were talking about technical hurdles, and our start-up and business experts were talking about commercialization hurdles… and then, even if you can make it through these technical and commercial hurdles all the way to the public sphere, maybe the market isn’t ready for you… maybe policy isn’t ready for you… maybe there are going to be legal issues. I find it interesting that you seem to be coming at it from such a broad perspective.

Maybe the environment in which SRI operating is so challenging because there are so many perspectives. We’re trying to actually cross this valley of death, but all the while we’ve got multiple market perspectives, we’ve got the inventors, and we have all this investment that we have to do to get it ready. How do we know how to invest in the right way? It really seems like a big problem…  The person who will grow a company is not the same person who can get it started, and it’s not the same person who can do the initial invention, or the person who can do the engineering effort to get the invention ready for market … it’s very rare to find somebody who can follow something from the very beginning to the very end.

So there is this broad set of competing interests, but the driving perspective for SRI is to align resources with what it takes to be successful – and that’s key. Really understanding what’s going on, being connected to the market by being connected to the technology, having the right people doing the right things… yes it’s broad, but there’s a single focus on finding solution sets, and one thing I’ve learned in this job is that you succeed best by putting people in a position of strength for them.

So where is your work initiated? Are you mostly assisting inventors who are seeking investment? Or are you looking for technical solutions for government and policy-makers?

If you look at just the business part of what we do, our main revenue comes from our contracts to do research. Our customers are organizations that want to fund research and development, and so either they come to us because they have a problem that needs solving and they don’t know have the resources or people to solve it, or they come to us because they know we have technologies that could solve their problem and they want us to develop our solutions further in order to suit their purposes.

On one side, we operate in this business development environment that is essentially being 100% on soft money. We have no endowment or annual corporate budget. If you don’t get the business development right then you are out of business after one year. And so you also have to be really good at evaluating your technology and qualifying the people and organizations that want to fund you to develop them. Part of my job as director is to figure out who the chronic innovators are, evaluate who could possibly fund them, and then do the work of building the relationship and making sure that everyone understands the market needs.

Basically, we are inventing new kinds of technologies that we believe will solve fundamental problems that are aligned with our customers’ needs. In the case of the robotics program at SRI, DARPA is a key customer, and one of their needs is to maintain the US lead in various applications of robotics technology. As a result, we work with them in a very collaborative process where we are educating and interacting and supporting that mission all at the same time.

Why does DARPA need SRI? Couldn’t they just work directly with the innovators themselves?

In rare cases DARPA would work directly with an inventor, but it’s a government agency, and it’s not easy to manage a government contract and to meet all of the costing and security requirements. SRI provides the overall support structure for managing the business relationship with the government. The other part of what we do is managing and documenting the intellectual property.

It goes back to what I was saying before that it’s very rare to have a single individual who has the aptitude to do all aspects of the technology transition process from invention to commercialization. So in an organization like SRI, we have people who are strong in business development teamed up with people who are strong innovators and people who are systems applications experts. By putting teams like this together, you can actually create a better market or industry response.

Do you have a formula or process for working out risk versus reward in terms of the research you undertake and the specific technologies that you select to move forward?

SRI definitely has internal processes for defining the value in its technologies and how it communicates that value. We have something we call “the discipline of innovation,” which is a series of processes that, when applied, allow you to be more successful in transitioning the technology. These include simple things like identifying a champion (and the champion may or may not be the inventor, but is someone who believes in the application of the solution and is personally committed at some level to being successful), and making sure that all the resources in an organization are aligned for success.

We also have a very clear process for communicating the value of our innovations to the clients – and that’s something we call ‘NABC’, which is really just a structured approach to communicating value to the customer. N is the need, so we define the specific need back to the customer to make sure that we understand it. The A is the approach, the actual technical solution. Us hardcore technical personalities love to focus on this part, and what often happens is that we either have a technology looking for a solution or we have the right technology but we focus so much on describing the approach that we forget to really look at the need and align it better with what’s required to be successful… so having a balanced approach is important. B is the benefit: having a very clear understanding of what the benefits are, and at what costs, and this is relative to C, the competition.  We go through this process in a very structured way, and it creates a much better solution set and sense of clarity to our technical and business development staff when they’re beginning to look at whether to apply a specific technology to a problem.

The other thing I’ll add is that there is a culture of iteration at SRI, and its understood and practiced through open communication and assessment of value, so that an NABC is not a single static document – it is a dynamic document that’s constantly being revised.

What would you say are the next breakthrough areas for robotics?

I think there’s a very clear progression that is occurring right now, and there are two different tracks that are going to be coming together in the near future.

One of them is that there’s a drive for robotics technology to become more low-cost and more accessible. A lot of people think about robotics as an independent technology space, and at some level it is. But if you really look at the current state of robots that are getting penetration, like drones/UAV’s and mobile robots, the emergence of these technologies has a lot to do with the fact that their components are cheaper and more available than ever before. And part of that is not because of robotics per say, but it’s because of other markets. In particular, the cost and availability of many sensors is driven by growth in the personal and mobile computing markets. Robotics is drafting off the last 30 years of evolution of personal computing technology, and represents the next stage in that evolution.

The second track is manipulation solutions: robotic arms and hands that allow you physically manipulate the world. If you look at the trends again, you’ve got lots of mobile robot solutions and emerging tele-presence platforms, which generally cost under $10,000 – and some of them cost a couple of thousand dollars – but they’re all lacking manipulation.

There’s been an enormous amount of investment from DARPA to develop lower-cost robot arms and hand solutions, and in the next three to five years, these manipulation technologies will begin to emerge.  And when the cost of these is lowered to the extent that they become accessible to entrepreneurial, hobby-level roboticists, that’s when we’ll start to see a much broader dissemination and application of robotics into a personal robotics market, similar to what happened in the early 1980s with personal computing. That convergence of low costs and manipulation technology is part of an ecosystem that is going to drive innovation in the next three to five years.

Is access to lower-cost equipment making it less risky to invest in robotics now?

It’s not going to make it less risky from an investment perspective because the overall funding required for a company developing something to the point of commercial viability is so much more than the cost of the product hardware components. And often the real risk is with the management team’s ability to properly set up the business model. But it will create products that have a greater potential to get penetration because they can be offered at a lower cost to the end user. Lower cost robotic components will also enable more entrepreneurs to start companies.  So, I would say that the real benefit is that more robotics companies will get started, which will ultimately lead to more successful companies and broader uses of robotics.

What other factors are contributing to this rapid growth in robotics?

The number one place to look is DARPA. If you go back in the entire history of robotics innovation, and look at which technologies are emerging right now in the market and what their origins were, DARPA was the inspiration and funding driver for almost all them at their early stages. It’s really pretty dramatic if you think about it.

Some people look at DARPA programs and think, ‘What are those crazy scientists up to now?’ but every one of those programs represents tens of millions of dollars of R&D funding, and these awards are being made to some of the top people in the country. If you look closely at who those researchers are, what their programs have been doing, and what they are being funded to do, you’re going to see the future of robotics.

With the rapid economic development of the last twenty years resulting in the accumulation of great wealth, China urgently feels the need to move from a manufacturing-driven economy to an innovation-driven one. As a result, China is supporting many bold research initiatives in an effort to develop and attract the highly skilled individuals who will be needed to lead this transition. Thanks to recent dramatic developments in hardware and software, economists anticipate that the Chinese robotics industry will meet its spring season this year.

Every five years, the central government of China releases a new Five Year Plan, which establishes the core concept of development in all areas of Chinese society. In 2011, China started its 12^th Five Year Plan, and for the first time ever, the service robot was ambitiously identified as an important area for development. As such, it is anticipated that many new Chinese robotics companies will emerge in the near future to meet the growth targets set out in the plan.

However, due to the economic crisis, the situation is changing subtly. I traveled around several main cities in China in early 2013 to learn from experts in the current automation industry, and it was apparent that because the new generation of Chinese workers asks for higher pay and lower workloads, it is very difficult for China’s massive manufacturing industry to recruit new workers. Not surprisingly, leading foreign industrial robot companies, such as Kuka, ABB, and Fanuc are taking advantage of this problem to actively market their robots to the manufacturing sector.

Local companies are likewise developing Chinese industrial robots that may have relatively low performance, but which are nonetheless able to meet industrial requirements. One such example is the Million Robots Project from the Chinese electronics manufacturing giant Foxconn. Yet, while the industrial robot market is growing, service robots still remain the province university labs and research institutes.

Funding high risk research in China
In China, almost all high-risk, high-reward research is conducted by central government agencies: the National Natural Science Foundation of China (NSFC), the Ministry of Education (ME), the Ministry of Science and Technology (MST), and the Organization Department of CPC Central Committee (ODCCC).

NSFC aims to support outstanding young researchers through the awarding of modest grants from the Young Scientists Fund. This is the most popular funding mechanism for young researchers when they first begin conducting independent research as it is relatively easy to apply for and receive. The Ministry of Education manages several projects, such as the Research Fund for the Doctoral Program of Higher Education of China (for new teachers), and the Chang Jiang Scholars Program (CJSP), which is supported by the Program for New Century Excellent Talents in University (NCET). For example, the CJSP tends to recruit top young and mid-career researchers both in and out of China to work as academic leaders, and awards sizeable grants to them.

The Ministry of Science and Technology manages several projects that are significant to researchers engaged in high risk, high reward research, including the National Basic Research Program of China (973) and the National High Technology Research and Development Program of China (863). The influence of 973 and 863 on Chinese scientific research is far-reaching, and only top-tier professors with excellent teams are considered for funding by this elite program. Because research funded by these projects is carried out by large groups of people, 973 and 863 usually give out significant funds, in the order of millions of dollars depends on the scale of the project.

The ODCCC funds high-risk research initiatives through the Thousand Talent Project (TTP), a three-year term project with possible extension. The goal of the TTP is to recruit thousand of foreigner researchers with strong expertise in hardware and software to help develop innovation in China. There are already more than 100 foreigner researchers working in China since 2008, the year TTP started, including Prof. Toshio Fukuda, a former IEEE RAS president, director of the IEEE Division X, who was invited to Beijing Institute of Technology (BIT) to work on Micro-nano research.

Where the money goes
It can be difficult for people outside of China’s borders to learn about the projects these granting programs support because, due in part to language barriers and visa restrictions, Chinese researchers tend to participate in domestic conferences rather than international ones.

Yet while robotics researchers from around the world keenly watch videos of Boston Dynamic’s Bigdog walking freely on uneven and slippery mountain roads, or Honda’s Asimo dancing and hopping on one foot, the Chinese government is also busy establishing similar projects in a more low key fashion (see this PlasticPals YouTube playlist for a fair sample of what the whole must be).

Having visited Chinese laboratories and spoken face-to-face with researchers, I have witnessed several bold Chinese robotics initiatives that are currently underway. One particularly challenging area of development is the humanoid robot, and two of the best robotics laboratories for this in China are at the Beijing Institute of Technology (BIT) and Zhejiang University (ZHU), both of which have succeeded in producing the first humanoid robots that can perform Tai Chi and play Ping-Pong (see here and here). These robots were not specifically designed for game play – they were in fact developed as part of China’s domestic service robots initiative – but Tai Chi and Ping-Pong have helped researchers to test their robots’ image processing and dexterity.

I visited BIT’s robotics lab this past April, and saw their BHR-4 and BHR-5 humanoids in action. Using cameras couple with high-speed image trajectory processing software, a pair of BIT’s BHR-5 Humanoid can rally at Ping-Pong up to 200 times without error. The BHR-5s stand 162 cm (5’4″) tall, weigh 63 kg (139 lbs), and have 32 degrees of freedom. With its realistic animatronic face, BIT’s BHR-4 Humanoid, on the other hand, is more focused on conveying a variety of emotions that on speed and gross-motor dexterity. It reminds me of Prof. Ishiguro from Osaka University, Japan, who is the world leading robotics researcher in building humanoid robots with lifelike appearance and visible behaviour. The BHR-4 stands 170 cm (5’7″) tall, weighs 65 kg (143 lbs), and thanks to all the moving parts in its face, has an incredible 43 degrees of freedom.

China is also pursing its own version of BigDog. In 2011, the 863 program established another 3-year project with funding up to $700US million that aims to build a Biomimetic Quadruped Robot like Boston Dynamic’s BigDog. Almost all the important robotics labs in Chinese universities are competing for this funding. The Harbin Institute of Technology (HIT), Shanghai Jiao Tong University (SJTU), Sheenyang Institute of Automation Chinese Academy of Science (SIA-CAS), Shan Dong University (SDU), and other universities and institutes each released their version of BigDog one after another.

These homegrown initiatives may not yet have the performance capabilities of their better-known counterparts, but the Chinese are well on their way to catching up to the world leaders in robotics research.

With contributions by Hallie Siegel.

If you liked this article, you may also be interested in:

• Swiss to invest almost CHF30 million in digital fabrication research over next 4 years
• Robot tourism coming soon to Korea: Masan Robot Land project finally breaks ground
• Russia looks inward and outward at Moscow’s 2013 Open Innovations Forum
• Long view required to keep economic policy in step with developments in robotics

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

Neurorobotics is one of the most ambitious fields in robotics and will play a major role in the newly announced Human Brain Project. This project was selected by the European Commission as a flagship project and will receive a prospected funding of 1 billion euro for a runtime of 10 years. The goal of the Human Brain Project is to pull together the highly fragmented knowledge in the neurosciences and to reconstruct the brain, piece by piece, in supercomputer-based models and simulations. It should lay the technical foundations for a new model of brain research that is based on Information and Communications Technologies (ICT), driving integration between data and knowledge from different disciplines, and catalyzing a community effort to achieve a new understanding of the brain, new treatments for brain disease and new brain-like computing technologies.

The Human Brain Project will pursue four goals, each building on existing work, and acting as a catalyst for new research:

1. Data: generate strategically selected data essential to seed brain atlases, build brain models and catalyze contributions from other groups.
2. Theory: identify mathematical principles underlying the relationships between different levels of brain organization and their role in the brain’s ability to acquire, represent and store information.
3. ICT platforms: provide an integrated system of ICT platforms providing services to neuroscientists, clinical researchers and technology developers that accelerate the pace of their research.
4. Applications: develop first draft models and prototype technologies that demonstrate how the platforms can be used to produce results with immediate value for basic neuroscience, medicine and computing technology.

We at the Technische Universitaet Muenchen in Prof. Knoll’s research group will coordinate the part of the project that focuses on “Neurorobotics”. Neurorobots are robotic systems that are comprised of a controller, a body, actuators and sensors, and whose control architecture is derived from a model of the brain. In the Human Brain Project, they are controlled by generic brain models that will make it possible to study them in closed-loop experiments: that is, experiments where the brain is inside a body, embedded in a realistic environment where the robot’s actions influence future sensory inputs. It is within the Neurorobotics subproject that the feasibility of the conceptual models will first be tested with simulated and real systems.

To design a robot for use in such an experiment, researchers will connect models of sensors and actuators to a brain model, calibrate the robot brain so that it can process the relevant signals, and translate the model’s neural activity into control signals for the robot. They will then use classical techniques (lesion studies, manipulations of neurons etc.) to identify the control architecture for specific tasks. The advantage of this approach is that it allows researchers to monitor and control all states and parameters of the experiment (brain, body, and environment) – something that is technically impossible to achieve in the laboratory.

We will be developing a Neurorobotics Platform that will offer scientists and technology developers the software and hardware infrastructure they will need to connect pre-validated brain models to detailed simulations of robot bodies and environments, and to use the resulting neurorobotic systems in in silico experiments and technology development.

Work in neurorobotics will lay the foundations for a new generation of computing systems and machines with cognitive capabilities that are absent in current technology, including a degree of autonomy and an ability to learn. This raises the issue of legal liability when a neurorobotic system injures humans or their property – a topic already raised by the advent of autonomous vehicles. The Human Brain Project Ethics and Society Programme will contribute to this debate.

The project has the potential to produce economically and socially valuable results even before it achieves its ultimate goals are met. All the planned technology platforms will be operational within the first thirty months. The first informatics-based techniques for the diagnosis of Alzheimer’s and other neurological and psychiatric disorders will be available before the end of the ramp-up phase (first 2.5 years). On the same time scale, the project will develop the first large-scale neuromorphic computing systems based on simplified, yet biologically realistic models of brain circuitry, and the first neurorobotic systems for use in closed-loop experiments and applications development.

The project will start in just a few weeks, so follow us on Twitter (@HumanBrainProj), Facebook (Human Brain Project), Google Plus (Human Brain Project) and the Frontiers Research Networking Community.

For more information, visit the Human Brain Project website, or download the The HBP Report Excecutive Summary.

Tweets by @HumanBrainProj

The Distributed Robotics Garden, a project of the Distributed Robotics Laboratory, CSAIL, MIT.

There’s much more to agricultural robotics than aerial systems, of course. There are already GPS-guided auto-steering systems on the ground that, when installed on tractors, assure complete coverage of fields with a minimum of overlap from one pass to the next while relieving stress on the generally still-present human operators they may one day replace. This same technology is finding its way into combine harvesters and other large, self-propelled equipment, enabling them to complete their tasks more quickly, using less fuel and reducing waste.

Application of chemicals metered according to need will further reduce waste, and robotic planters are already producing more even stands using less seed. In milking parlors, robots are taking over the tedious, noisome, and somewhat dangerous job of milking cows. Robots that tend vineyards and orchards, pruning and mowing between vines and picking apples, are either already available or well along in their development. Moreover, under pressure from labor shortages, efforts are underway to mechanize or automate labor-intensive aspects of the production and handling of a wide range of speciality crops, from asparagus to strawberries, if not in the field then in preparing the produce for shipment.

Two alternative approaches to the application of robotics to agriculture are hidden in these developments:

1. The robotic control of heavy equipment in field operations: This is essentially an extrapolation of what’s happening at present, but executed with greater precision.
2. Robotic replacement for hand labor: The level of detail required in mapping and manipulation the environment opens the possibility of a new, robotics-intensive approach, initially enabling better practices in the raising of speciality crops, but eventually scaling to replace the methods currently used on large acreages. This robotics-intensive approach is a scenario that involves both significant investment and significant disruption as it scales upward to larger areas and staple crops with thinner margins.

The simple extrapolation approach doesn’t alter the fundamental nature of agriculture as it is commonly practiced. It treats robotics as an add-on or substitute rather than as a transformative opportunity. The obvious risks, involving investment in engineering and in purchasing equipment, while significant for those immediately involved, are finite, estimable, and almost entirely financial in nature. The rewards are similarly modest: for equipment manufacturers, it amounts to keeping up with competitors; for farmers, there may be a slight improvement in margins and maybe a little more time to spend not sitting on a tractor.

The hidden risks with this approach are longer term, and relate to the perpetuation of agriculture as currently practiced, with all of the environmental and nutritional problems that implies. A robotic tractor is still a tractor, compressing the soil over which it passes, necessitating tillage to re-loosen it, consuming energy and exposing the soil to erosion and accelerated mineralization, and reducing its ability to supply nutrients to plants and its ability to absorb and retain water. With a rising human population and finite arable land, the risks of using this approach include the possibility of widespread malnutrition and more frequent famine.

The robotics-intensive approach, which I like to term Cultibotics, but which I’ll simply refer to as ‘robotic gardening’ here, leverages robotics in order to transform current agricultural practice. To accomplish this, the machinery must become autonomous so that a single farmer can oversee the simultaneous operation of many machines. The one-to-one correspondence between operator and machine has driven the increasing size and power of tractors and other machinery, and breaking that correspondence is a necessary first step toward a robotics-intensive approach. Given autonomous machines, and the resulting possibility of operating many of them, the use of much smaller, lighter machines becomes an option. If these machines are also capable of dextrous or otherwise detailed manipulation, an altogether different approach to agriculture becomes possible: one based on the best practices of gardening, applied on a grand scale.

If you’re having trouble imagining what I’m referring to here, consider the Telegarden.

The Telegarden, as installed at Ars Electronica, 1995-2004, Photo courtesy of Ken Goldberg

The Telegarden wasn’t autonomous; rather it was remotely operated by many people in succession. It also wasn’t light or mobile, as it used a pivoting industrial robotic arm to perform all operations. It wasn’t even a particularly serious attempt at gardening, since there was no overall design or production goals driving the planting decisions. What it did was to demonstrate that a robot can perform the physical tasks necessary to maintain a garden. Gardening robots could come in various sizes, some larger and some smaller, could be mobile, and could operate autonomously under the direction of a coordinating system, itself guided by a plan created by the gardener or farmer.
Because they could do their work in a detailed fashion, planting one seed at a time, or pulling one weed at a time, instead of moving soil around in bulk, and because they could have the luxury of operating continuously (24/7 if need be) instead of having to race to get the job done quickly, their power requirements would be modest — well within the bounds of what could be supplied by device-mounted solar arrays and/or on-site wind generators — and the need for energy storage could be minimized by saving operations requiring less power for the nighttime. Some of this technology is already in development, but the preponderance of the work remains to be done, which is to say that there are still many ‘ground floor’ opportunities available: designing gardening techniques suitable to robots, developing hardware to perform those techniques and platforms upon which to mount that hardware, and software to drive it all, both at the device level and for the coordinating system.

Obviously there are risks in this scenario as well: the cost and time required to develop the technology, and then additional time to ramp up production and gradually replace existing equipment. By the time this has been done, some other solution, perhaps the cultivation of algae in water-filled glass tubes, might be well on its way to replacing agriculture altogether. There’s also the risk that only a small percentage of farmers will be willing to give up familiar practices to take advantage of the new technology and the new approach it enables, and a risk that some corporations will choose to resist the change rather than adapt to it.

The benefits, on the other hand, potentially include: the transformation of a monoculture-dominated landscape into a huge garden; reduced reliance on petroleum-based products; improved diets through the extension of mechanization to crops like tomatoes, peppers, strawberries, etc. (making them more affordable); dramatically reduced loss of soil to erosion; reduction of agriculture’s contribution to CO2 production and reversal of the loss of soil carbon, helping to soak up some of the extra CO2 already in the atmosphere; increased system resilience for a more robust response to climate change; and increased respect, and a far more interesting livelihood, for farmers.

For the companies and investors who take the financial risks to bring this about, because much of the savings in reduced fuel and chemical use become available for equipment purchases, there is the potential for a huge payoff.

For society at large, there’s also the economic stimulus of a new industry, one with the subtlety to bring technology to bear in the service of soil fertility, sustainability, and biodiversity, as well as the production of food and other plant-sourced products.

For many people, the term “innovation” implies having a great idea, and hoping that somehow it will take off. According to H. Chesbrough, this is clearly insufficient, and in his definition, he is very specific and demanding: an innovation is an invention that has been developed into a novel product or service, and which is creating economic value. Or simply stated: Without market success, no innovation has happened!

The importance of innovation and startups for a country’s economy
As most companies know from painful experience, the market success of novel products or services is not a given. According to Chesbrough’s definition, innovation therefore implies a high risk of failure. These risks tend to remain small as long as the innovation process is proceeding in incremental steps. Such “sustaining innovation” is the realm of established companies, which excel at deriving large value from small changes. However, if a completely new and different value proposition suddenly appears in a so-called “disruptive innovation”, established companies are frequently ill positioned to exploit the arising opportunities.

According to C. Christensen, established companies are then faced with the “innovator’s dilemma”: their existing customer base and company expertise are too far removed from the requirements of disruptive innovations. This really is the realm of startup companies and innovative SMEs because they are less risk-averse, more flexible and less mired by entrenched company traditions than well-established, already successful firms.

In effect, startups have also a major function as job creators in a country’s economy. As shown in an influential study by the Kauffman foundation, startup companies in the United States reliably create many more jobs than existing companies, and they do not show the economic fluctuations (net job destruction during recession periods) of existing firms. As a consequence, it is a major task of society and its universities to encourage and to support the creation of startup companies, in particular in the high-tech domain.

Fostering entrepreneurship at a university
What should a university do to foster entrepreneurship, beyond providing a solid technical education to its students? Finding and implementing good answers to this fundamental question is of particular importance to ETH, which is the number one university in continental Europe, according to several global university rankings. Since ETH aims to have maximum practical impact with minimum effort, the obvious thing was done: Successful entrepreneurs who had built thriving enterprises out of fledgling ETH spinoffs were asked what measures would have helped their companies to grow faster, hire more personnel and create more economic value. Without aiming for scientific rigor in our informal query, we were surprised to learn that successful ETH entrepreneurs consistently mentioned three key support measures making a large difference for a young entrepreneur:

1. Peer home:  Young scientists usually feel very much at home at their university institute, where every scientific or technical question is immediately answered efficiently and exhaustively. However, as soon as entrepreneurial issues are raised, the interest of other young scientists quickly vanes. As a consequence, young scientists with entrepreneurial ambitions are looking for a second, entrepreneurial home at their university, where they can find like-minded young talents. However, it is not only peer support that they expect to find in such a “biotope” but also peer stimulation and peer pressure: it is well-known that the most effective way for people to learn is from good (and bad) practical examples given by one’s peers.
2. Experienced coaches: It takes many years to become an experienced business person, to master the countless things it takes to build successful enterprises, and to develop a deep understanding of specific markets. For this reason, young talent can profit enormously from the practical advice offered by an experienced and successful serial entrepreneur. One should not underestimate the ability of a well-chosen coach to be a compelling role model, who passes on significant behavioral and ethical values in addition to technical and business know-how! For this reason, it is vital that the “chemistry” between coach and coachee is perfect, and a substantial effort should go into good matchmaking.
3. Network facilitation: The combined professional network available at an excellent university is enormous. Unfortunately, young entrepreneurs usually find it quite difficult to tap into this huge network – either because they do not know whom to ask, or what and how to ask. As a consequence, young entrepreneurs need “network facilitators” who have both the experience and the authority to make a university’s vast network available to the young talents with high effectiveness.

The ETH Innovation and Entrepreneurship Lab (ieLab)

The desired “innovation biotope” for the ieLab was realized as several large open offices with associated infrastructure, where young talents can find a second, entrepreneurial home at ETH. We found that the different business cultures in various fields necessitated the creation of different ieLab open offices: one for typical engineering activities (including a mechanical work-shop), comprising robotics, automation, micro/nano-electronics or photonic microsystems; another one for life sciences (including well-equipped biochemical wet-labs), where development cycles are usually much longer and a trade sale exit to a large pharma/chemical company is rather the norm than the exception; another one for ICT startups with their characteristic very short time to market.  Each ieLab typically hosts eight to twelve teams.

The ieLab coaches are experienced and successful serial entrepreneurs, hand-picked from ETH’s vast network to match the personality of the young talents. The coaches share their large experience, business practices, market intelligence, supply chains, customer bases, investor communities etc. with their coachees, introducing them very effectively to all aspects of today’s high-tech entrepreneurship.

In particular, coaches have a deep knowledge of their respective markets – they know about the needs of potential customers and about the shortcomings of current solutions, and they are good at figuring out which novel technological solutions could have large market potential. Also, the coaches are instrumental in an often-overlooked aspect of the development of a startup: it has been found that fledgling high-tech startups grow faster and create more economic value if they have entered into a strategic R&D alliance with an established industry partner. The experience and negotiation acumen of an adept coach can help enormously in the creation of a win-win alliance partnership between academic spinoff and large industrial company.

The ETH network facilitators are all very experienced in the startup process and they have a vast network at ETH and other leading universities. Since they have their desks right in the middle of the open spaces, there is no entry barrier for the young talents to ask for help and to watch how problems can be solved effectively.

In addition to operating the ieLab, ETH offers a range of support services for the fostering of entrepreneurship, most notably ETH transfer, the tech-transfer office of ETH. All questions regarding contracts, legal issues, intellectual property, licensing, etc. are answered by ETH transfer’s professional crew. Several types of events related to entrepreneurship are regularly organized, including the ieLab weekly lunch seminar, industry days, homecoming dinners for ETH spinoffs, the Spark Award, “meet the pioneers” events, partnership council meetings, etc. Finally, ETH offers Pioneer Fellowships to the most gifted 10-15 young entrepreneurs each year. This includes CHF 150,000 seed money, free incubation in one of the ieLabs, free access to the services of ETH transfer, as well as individualized free coaching and networking throughout the 18 months of the Fellowship.

Entrepreneurship in the ETH curriculum
At ETH, entrepreneurship is not a set of isolated activities; it is an integral part of the ETH culture. This is particularly evident in how the entrepreneurial spirit is fostered early on in a young researcher’s studies at ETH, with Focus Projects, for example. The students in several departments have the choice how they spend their last bachelor year: either in the conventional way with a full set of lectures, or in a focus team with fifteen to twenty like-minded students, usually from different departments. Only a reduced set of lectures must be taken – much of the time is devoted to a “grand challenge”, where the team often tries to break a world record. The students must solve all problems themselves, only lightly coached by experienced advisors. They must organize themselves, plan their project and monitor progress, find suitable sponsors for their project, control their own budget, communicate with other teams and the media, and work extremely hard towards a common goal: “Demo or die” on rollout day, when they either demonstrate that they have jointly stood up to their challenge, or that they have failed together…

This experience makes great team players out of initially individualistic students; they have learned how to acquire additional theoretical knowledge effectively and how to apply it rapidly to their practical problems, they have become flexible problem solvers, and – last but not least – they realize their entrepreneurial potential.

A case in point – Robotics innovation “Skye”
A good example of how innovation and entrepreneurship have become deeply ingrained into the ETH culture is the robotics innovation Skye. It all started with a difficult challenge to be tackled in the framework of an ETH focus project. The problem was to come up with a robotic solution for the safe monitoring of large crowds or complex technical facilities, making sure that no dangerous situation could arise due to the possible malfunction of a component or sub-system.

The challenge was embraced by a dedicated team of nineteen students from diverse ETH domains such as robotics, mechanical engineering and electronics, as well as industrial design students from the Zurich University of the Arts. The students came up with a novel concept of a surveillance robot: an omnidirectional spherical blimp capable of travelling at speeds of up to 20 km/h and at cruising altitudes up to 500 m above ground. Thanks to its helium-filled hull, the Skye can stay airborne for longer than an hour; the four main steerable electrical motors on the 2.6 diameter blimp are merely used for movement and stabilization against wind forces. The Skye project was supported financially and received expert advise from Disney Research Zurich, the ETH Autonomous Systems Lab, the Linde Group (PanGas), Maxon Motors, and several other Swiss companies.

As a consequence of their successful focus project, and thanks to encouraging feedback received from potential users of their innovation, a half-dozen members of the original Skye team decided that Skye could become a business – and that they could become successful businessmen!

Today, the core Skye team is following up on this challenge to create economic value out of their research project, and they are supported in this quest by the ETH ieLab. The Skye team members are maturing their entrepreneurial skills, they are improving the original concept, and they are applying it to practical problems. Their goal is for Skye to become the most safe and energy-efficient solution for demanding surveillance applications such as monitoring large crowds, complex technical installations, high-rise buildings, agricultural crops or geological/archeological sites.

The experience of the Skye team is similar to that of other young researchers at the ieLab: entrepreneurship is not just about learning management techniques and tools – it is about undertaking challenging tasks and accomplishing astonishing things!

References:

H. Chesbrough, “Open Innovation – The New Imperative for Creating and Profiting from Technology”, Harvard Business School Press, Boston (MA), 2003.

C. M. Christensen, “The Innovator’s Dilemma”, Harper Business Essentials, NewYork, 2000.

T. Kane , “The Importance of Startups in Job Creation and Job Destruction”, Kauffman Foundation Research Series: Firm Formation and Economic Growth, Ewing Marion Kauffman Foundation, July 2010.

R. B. Cialdini, “Influence – The Psychology of Persuasion”, HarperCollins Business Essentials, New York, 2007.

S. Hess, “An Open Innovation Technology Transfer Concept – R&D Cooperation for Breakthrough Technologies Between Academic Spin-Offs and Established Industry”, Dissertation ETH No. 20731, 2012.

Examples of ETH focus projects are described at http://www.asl.ethz.ch/research/focus

Skye project website: http://www.projectskye.ch

Often funding sources – the groups taking the risk – are not the beneficiaries of the rewards of the venture. Intuitive Surgical is an example.

NSF, DARPA and NASA funded a project to solve a very real problem: providing medical attention to Americans in remote places such as space, war or scientific expeditions. The initial concept was to be a telepresence project but with no known solution. That was the high-risk research project funded by the three agencies.

During the development phase, two enterprising team members, Frederic Moll and Robert Younge, concluded that true remote-presence surgery couldn’t be achieved with then-current technology but could be achieved within a controlled network,and by so doing there would be many benefits to the surgeons and patients involved. The two wrote a business plan, got venture money and proceeded to develop the system now known as the da Vinci Surgical System within a company subsequently named www.

Shortly before going public, and before getting FDA approval, Intuitive Surgical was sued for patent infringement by Computer Motion, Inc., which had its own system, the ZEUS Robotic Surgical System – a system which was already approved for use in Europe.

The litigation was resolved in 2003 when the two companies agreed to merge, thus ending the litigation. The ZEUS system was ultimately phased out in favor of the da Vinci system. The litigation created an active depository of intellectual properties – hundreds of patents and trade marks – that Intuitive Surgical has vigorously defended in a way that has created a competitive void in the commercialization of similar devices and systems.

Consequently Intuitive Surgical has grown to be a growth company with large profits and is listed on the NASDAQ stock exchange (NASDAQ:ISRG). But it didn’t exactly happen with the high-risk / high-reward formula. Intuitive Surgical and it’s VC backers got the reward and NSF, DARPA and NASA are still searching for their solution.

SRI and Stanford University did get money for licenses to use the technologies they developed, but not the huge rewards stemming from the successes of Intuitive Surgical.

As is often the case in startups, the solutions and products that finally emerge in the marketplace are ones that are different from those that initiated the startup. Sometimes this involves changes in funders, partners and ownership and those that reap the final rewards are often removed from the originating project.

High risk takers are most often governmental agencies with a strategic social/political issue to resolve. Corporate R&D departments also invest in future scenarios but to a lesser extent – they most often pragmatically acquire the technology they need and rework it to fit their specific requirements.

Hence the dilemma: does the initial risk taker expect to get the double reward of (1) product solution and (2) profits from their sale, or just the altruistic reward of seeing that their initial problem, for which they are risking their money, is being addressed in the broader marketplace solving the original problem, reward or not?

Over the last 20 years or so, a sense that science has become conservative or incrementalist has developed, and calls for change in the approaches to public funding of research have been heard from various quarters. Several notions have been suggested of what should be supported instead of “normal science” or “incremental innovation.” Among them we have heard calls for more “high risk-high reward” research, or for more “highly creative” science, or for more “cutting edge” or “frontier” research and, more recently in language adopted by funding agencies, that more “transformational research” is needed.

The main idea is that there isn’t enough risk taking, and little scientific or technological creativity is reflected in proposals that might “revolutionize” our understanding of the nature or society. “Business as usual” in the support of science and technology seems not to be satisfactory for producing the sort of radical change and rapid advance in science needed to solve important problems we face in economic development, health care and general social wellbeing.

This discussion does not fully address the situation in the private sector. However, the fate of many top-performing R&D centers in companies such as IBM, Texas Instruments, AT&T, among others, over the same period indicates that corporate willingness to take risks in research is not precisely at its most adventurous. Having said that, our focus here is on public funding since the sense of risk and reward of innovation by private industry in the market does not have the severe principal-agent problem faced by public funding. Companies invest their own money and either lose it or make a profit for themselves. Researchers funded by government do not lose their own money in the risk taking. The government takes virtually all the risk, at least in the financial sense.

When it comes to radical innovation in robotics, this distinction about risk taking must be kept in mind. In any case, the participation of the public sector in the development of robotics technology has the purpose of reducing the risk for private companies that, without at least partial subsidies from government, would not have a safe enough business case to get involved. This is the well-known market failure justification for public support of R&D.

In the context of public funding, the capacity of the government to take on R&D risk is not infinite. And moving from the intuition on the desirability of more scientific and technological breakthroughs to a clear understanding of how it would be recognized, what exactly is the difference between incremental research and proposals for future scientific revolutions, and what the proper incentive and support mechanisms are for it has proven difficult. It is not clear what in the research process has the attribute that is looked for. Is it the projects that are special? Are certain scientists capable in a different way? Is it the organization that can provide an unusual environment for research? Or is it groups of projects, that is, portfolios that must be assessed for their combined effects? So far, none of these questions has a clear answer.

Models, Metaphors, Analogies to Produce Meaning
One way to begin to clarify the problem is to take the models that are being used either implicitly or explicitly to suggest courses of action. We have identified at least four that have appeared repeatedly in documents and speeches about the “special” research that might have higher impact. They work as analogies for highlighting one or more important attributes or their consequences. These are the models of stock investment portfolios; biological evolution; “hot” trends; and exploration of the wild.

Stock Investment Portfolios
Many calls to support more “high risk” research that has the potential to offer “high reward” appeal to the intuition of investment risk. In this analogy, each project that is up for support is similar to a stock considered for inclusion in an investment portfolio. As in stock portfolios, a balance of risk is sought across the portfolio so that a number of safer bets compensate for riskier ones that might payoff disproportionately.

The important implication of this perspective on research support is that the unit is the project and that each one is assessed in comparison with the others in the portfolio. The overall balance of risk of a good portfolio means that projects are not selected on the same criteria. Some will be “good” low risk projects to compensate for potential losses of the high risk ones. This is clearly not current practice in public funding of research since all projects are assessed on the same criteria and only the best are funded. All projects have approximately equal risk.

There are a few consequences that must be given further thought. First, what attribute of projects must be risk assessed on? Are risks across disciplines considered, for example? Or are risks across organizations or types of projects? No clear answer to this question has been offered. Second, how much failure will be tolerated? If risk is real, then high risk means high probability of failure and a high failure rate should be observed. Otherwise, the risk is not as high as it was assumed. No clear answer for this question is available either. Much talk about “high risk-high reward” is superficial and really suggests that most high reward projects could be identified with some special assessment mechanism without a high risk of failure. Actual failure would probably be pinned on the proposal assessment mechanism rather than the risky nature of the projects.

Evolution in Science
This analogy implies that new ideas in science appear much like genetic mutations do in living organisms. If our selection mechanism allows for the more radical mutations to survive, more radical change in science would probably follow. Most attention has been drawn to the selection mechanisms, assuming implicitly that the size of the pool of variation for the chance of selecting viable large jumps is sufficiently large.

The consequences of this line of thinking are also intriguing. First, the necessity of a random generation of variation in scientific ideas is not a familiar one. The image of a rational scientific method has a strong cultural effect of keeping random idea generation hidden from view, if not largely muted. In other words, attention would have to be put on the environment that allows for a large pool of very diverse scientific ideas to emerge continuously in order to have large jumps that might be selected. Secondly, a large measure of randomness in the consideration of scientific ideas would have to be acceptable to the establishment. Otherwise, just as the reduction in biological diversity is a serious problem for environmental health, there will be very little to select from to have many “viable new scientific organisms.”

Hot Trends in Science
Science is not different from other cultural phenomena in that novel trends generate “icons” and “hits” that the community rallies around. As a matter of fact, many new ideas in science and technology are followed during the first period of their public diffusion to assess whether they are substantive or only a fad. It is often heard of researchers gaining prominence in their fields that they are new “stars” or that they are “hot.” These trends and their icons attract a following, and researchers that join the movement fashion their professional identity around the key features of the emerging field.

The main lesson to drive home from the analogy is that this cultural phenomenon is an integral part of science even if it is not formalized into the assessment of proposals and projects. Individual projects are not the main focus. The “movement” is the main concern. But, how does the perception of a hot trend and its icons affect the assessment of project proposals that promise to continue in its wake? How does the system deal with the possibility of a fad that wastes energy and resources on a trend that does not pan out? A rapid recognition mechanism to tease out indicators of faddish elements seems to be necessary to address this issue. But this would be a “conservative” reaction. Should the system be more liberal in embracing hot trends to avoid killing off potential revolutionary developments? This would be another type of risk that might have to be accepted. With its acceptance comes another departure from a common view of rational science that looks askance at enthusiasm and bandwagon effects as illegitimate passions. Maybe science should have a few “moments of madness”.

Exploration of the Wild (or the Endless Frontier)
The idea that science is a sort of exploratory venture into the unknown unexplored regions of nature is an old one. It was made into influential policy discourse by a prominent scientist in the aftermath of World War II: Vannevar Bush. It assumes that what we don’t know is contiguous with what we know, and we just have to “venture out” into this unexplored territory to get to know it. The only difference between conservative, incremental science and the highly creative or transformational research is that the exploratory journey should go deeper and farther into the unknown in one single expedition.

This image suggests that the main way to achieve sizable leaps to advance science is by organizing well-equipped and supported expeditions into unexplored territory. The skeptics in policy making recognize immediately that this analogy suggests that research always requires more funding: “If you give us more money, we can go deeper into the unknown.” This was already recognized as a subtext of the original idea of the “endless frontier.” Risk is downplayed in this analogy. Only underprepared expeditions are risky. There is always unknown territory to discover. The question is rarely raised that there may be little of value in vast regions of the unknown.

A Path Forward?

It seems that before we can define a path forward it is necessary to come to terms with the full consequences of what must be achieved. First, the simple formula of “high risk-high reward” hides the complexity of the goal. There are many interrelated issues that must be addressed simultaneously if this objective will be pursued with a committed effort. A simple set of criteria for evaluating proposals will not do the job. Second, a part of the public image of science as a highly rational activity is tied to the status quo of low risk incremental change in science. Higher risk and randomness that seem to be inherent in increasing the magnitude of change in science may open the enterprise to criticism and loss of legitimacy because of an appearance of irresponsible gambling with public resources. The idea is that the payoff of a few initiatives that succeed will compensate for many that fail. Is there any publicly supported system in today’s political environment that can operate legitimately under those conditions? Not likely.

The DARPA Robotics Challenge has an interesting approach to encourage the pursuit of “leaps forward” in the development of these technologies while reducing how much risk it takes on at any point in time. It does so with two crucial measures. First, it specifies a well defined target of technological capabilities. So, in terms of the metaphors we used, it is not looking at the entire evolutionary environment of technology or at the entire frontier of technology. It limits how much randomness must be accepted and how far into the unknown it may be necessary to travel before something of value is shown. Second, by making several teams compete in stages and granting a limited amount of funding to compete in progressively more difficult challenges, it reduces potential losses and requires the return of something of value before more risks are taken. At most, no team will advance to the next stage, so what has been granted will be lost, but it cuts its future losses as soon as nobody can show progress. However, the risk of pursuing those technological objectives rather than others has not been mitigated, and the stages might cut losses too early.

For this reason, in fields where objectives cannot be specified with such clarity, program design is more difficult. But the lesson remains. As the DARPA program shows, the flexibility may have to be transferred to the very support mechanisms themselves. Attempting to derive a fixed set of procedures and criteria to identify and support something that is highly variable, with an irreducible measure of randomness, culturally unstable and insatiably adventurous may be the wrong path to go down. The criteria and support mechanisms may have to become “experimental” themselves. The future development of scientific and technological research may be pressing for some institutional change.

National Science Foundation (NSF) efforts to develop a mechanism to fund research proposals that had a high risk of failure, but which also had the potential for high return, began in 1980. In that year a task force was created by the NSF Advisory Council to look at the issue of “highly creative or innovative” proposals for which there was “a high risk of failure.”

The task force’s report identified two significant hurdles the NSF needed to overcome to support high risk/high return proposals: 1) the conservatism of the peer review process, and 2) the reluctance of NSF program officers to fund research with a high potential for failure in an environment in which proposals with a high probability of success were not being funded because of limited budgets. The task force suggested a variety of ways to encourage NSF support of these risky proposals, most of which depended upon giving the program officer encouragement to use his or her discretion to identify and fund such proposals. None of the recommendations of the task force were immediately adopted by NSF, although some of the mechanisms suggested appear in modified form later.

The first NSF mechanism for supporting high risk proposals was developed in the recently created Engineering Directorate in the mid-1980s. Engineering took the initiative because of the leadership of Assistant Director Nam Suh, who felt that a small number of failed grants were a necessary and unavoidable by-product of efforts to fund innovative research. The Directorate was also responding to recommendations by the National Academy of Engineering for NSF support of high-risk/high- return research. Engineering established a pilot project called Expedited Awards for Novel Research (EANR), encouraging program officers to fund innovative proposals without the benefit of peer review.

The successes of EANR funded proposals persuaded NSF Director Erich Bloch to establish the Small Grants for Exploratory Research (SGER) mechanism for the entire NSF in August 1989. Restricted to no more than 5% of any program’s budget, SGER awards, like EANR awards, bypassed the external peer review process. There were a number of objectives for SGER awards, but one was to induce researchers to “propose high-risk work to NSF.”

In 2004, the National Science Board established a Task Group on High-Risk Research to reexamine the issue. Its work, and that of an NSF staff task force on the topic, led to the replacement of the SGER award mechanism by a number of other mechanisms in 2009. The most important of these for high-risk research was the Early-concept Grant for Exploratory Research (EAGER). An EAGER award will provide up to $300,000 for up to two years of “high-risk, high-payoff” research. Like its predecessors, EAGER awards do not require external review.

References
National Science Foundation Advisory Council, “Report of Task Group 10: Funding of Innovative High Risk Proposals,” November 6, 1980
“NSF Starts Program to Encourage Creative Engineering Research,” NSF PR85-74, December 12, 1985
Erich Bloch, “Small Grants for Exploratory Research (SGER),” Staff Memorandum O/D 89-11, August 14, 1989
Dian O. Belanger, Enabling American Innovation: Engineering and the National Science Foundation, 1998, p. 246.
National Science Board, “Enhancing Support of Transformative Research at the National Science Foundation,” May 7, 2007
National Science Foundation, “Proposal Award Policies and Procedures Guide, Part I: Grant Proposal Guide,” October 2012

On June 17, 2013, Astronaut Chris Cassidy successfully drove a K10 rover on earth, via remote connection from the Surface Telerobotics Workbench on the International Space Station, showing that robots deployed to explore Mars or the far side of the moon could be remotely controlled by astronauts in space during future deep-space missions. Telerobotics, which involves human operators remotely controlling robotic arms, rovers and other devices in space, is one means of reducing risk in dull, dangerous or dirty tasks as humans explore space. 

NASA has a long history of playing for high stakes; think of the 7 minutes of terror Curiosity descent to Mars, Spirit & Opportunity, and indeed, the entire space race. Yet when human lives and millions of dollars in technology are invested, it’s critical to keep risk at a minimum. As part of our series on ‘High-Risk / High-Reward’ robotics, we asked Dr. Terry Fong of the NASA Ames Intelligent Robotics Group, to describe how NASA’s telerobotics initiatives help mitigate risk in space missions. – Robohub Editors

Photo credit: NASA. Chris Cassidy studies the Surface Telerobotics Workbench on the International Space Station to remotely operate the K10 rover on Earth at NASA’s Ames Research Center in Moffett Field, Calif., in June 2013.

Photo credit: Dominick Hart/NASA. NASA’s K10 rover at the Ames Research Center in Moffett Field, California performs a surface survey with its cameras and laser system, and then deployed a simulated polymide antenna while being controlled by an astronaut in space during a June 2013 test.

“Surface Telerobotics” is a 2013 NASA test to examine how astronauts in the International Space Station (ISS) can remotely operate a surface robot across short time delays. This test will be performed during the summer of
2013 and has three objectives:

1. To demonstrate interactive crew control of a mobile surface telerobot in the presence of short communications delay,
2. To characterize a concept of operations for a single astronaut remotely operating a planetary rover with limited support from ground control, and
3. To characterize telerobot utilization, operator workload and operator situation awareness.

Surface Telerobotics is intended to reduce risk for future human-robot exploration missions, identify technical gaps, and refine key system requirements.

In planning for future human exploration missions, numerous study teams have proposed having astronauts remotely operate surface robots from an orbiting spacecraft using a low-latency, high-bandwidth communications link. This concept of operations is seen as an effective method for performing surface activities that require real-time human involvement without incurring the risk and cost associated with human sorties. In addition, this configuration would allow high-performance spacecraft computing to be used for high-level robot autonomy (perception, navigation, etc.), thus simplifying the processing and avionics required for the robot. Crew-centric surface telerobotics is considered an option for several possible missions:

• Lunar Farside: Astronauts orbiting the Moon (or station-keeping at the Earth-Moon “L2” Lagrange point) remotely operate a surface robot exploring the lunar farside. Astronauts would take advantage of low-latency (less than 250 ms) and high-availability communications to maximize robot utilization during a short-duration mission.
• Near-Earth Object (NEO): Astronauts approaching, in orbit, or departing a NEO (e.g., asteroid) remotely operate a robot landed on surface. Astronauts would control the robot from the flight vehicle because the NEO environment (high rotation rate, rapidly varying illumination, etc.) rules out remote operations from Earth.
• Mars Orbit: Astronauts in aerostationary orbit around Mars (or perhaps landed on Phobos or Deimos) remotely operate a surface robot exploring Mars. Astronauts would control the robot from the flight vehicle when circumstances (time-critical activities, contingency handling, etc.) do not permit remote operation from Earth.

If successful, this project will help NASA better understand the key issues, engineering requirements, and costs/benefits associated with crew-centric surface telerobotics. In addition, data collected by the test will inform the design of future ground-based tests, particularly in terms of the key factors that need to be simulated at high levels of fidelity.

Finally, this project will help confirm (or reject) many of the assumptions and hypotheses that have been made by numerous space exploration study teams regarding the technology maturity, technology gaps, and risks (operational and functional) associated with crew-controlled surface telerobotics.

Project Partners
NASA Ames Intelligent Robotics Group
NASA Lunar Science Institute
NASA Technology Demonstration Missions Program
Jet Propulsion Laboratory
Lunar University Network for Astrophysics Research

Mission Location
International Space Station and the NASA Ames Research Center in Moffett Field California

More Information
http://tinyurl.com/surface-telerobotics
http://www.nasa.gov/telerobotics

Over the past two decades, robotic planetary exploration has generated an incredible wealth of knowledge about our neighbors in the Solar System. We now realize that celestial bodies within our reach can provide resources such as water, minerals, and metals, essential for sustaining and supporting robotic and human exploration of the Solar System. It is only matter of time before “living off the land” exploration enabled by in-situ resource utilization (ISRU) becomes a reality.  The Solar System offers almost unlimited resources, but the difficult part is accessing them. Thus, if the cost of mining and processing can be reduced, some of the minerals that are in high demand on Earth could in fact be brought back and sold for commercial gain.

In general, there are at least three bodies that have been seriously considered for resource exploitation. These include our Moon, Mars, and near-Earth asteroids. The Moon offers resources that could be used in-situ (such as water), and because of its proximity to Earth, other resources (especially rare Earth minerals) could be returned to Earth and sold. Resources on Mars could be used to sustain a human (and robotic) presence, and to fuel launches back to the Mars orbit and to other destinations. Asteroids are relatively easy targets because of their low gravity well, but the micro-gravity environment makes mining and mineral processing extremely challenging.

Knowledge of the vast resources in space has triggered an explosion of interest in space mining, especially over the last few years. NASA is working on several feasibility studies and architecture concepts for resource utilization from the Moon, Mars and asteroids. (Honeybee Robotics is contributing to some of these, including the Robotic Asteroid Prospector.) In 2009, NASA’s National Commission on Space study (PDF) argued that extracting resources from the Moon and asteroids will be essential for further space exploration and colonization. Private firms have jumped in with the goal of bringing space riches down to Earth. And with contests such as Lunabotics and the NASA Sample Return Challenge bringing new talent into the sector, training and education have expanded significantly.

What’s Honeybee’s role in this? We’ve been working on space mining and resource processing technologies for the past 25 years, providing feasibility studies, systems analysis, unique solutions and technologies, hardware development, test results, and flight systems for NASA and other space partners. Some of the approaches could be applied to both the Moon and Mars, but asteroid mining, because of the extremely low gravity field, is in its own category. Potential technologies would have to be thoroughly tested before they could be integrated into the asteroid mining spacecraft. Of course, the only true way to test new technologies is on a mission itself! The Keck Institute for Space Studies (KISS) recently determined that it would be feasible to bring a 7 meter, 500-ton asteroid near Earth by 2025, with asteroid identification, capture and return costing on the order of $2.6 billion (PDF). Such an asteroid parked at the Earth-Moon Lagrange Point would be an ideal testbed for new space infrastructure, proximity operations, space mining and resource processing technologies.

And while there are many challenges to a successful space mining program (economics, fuel, human safety, etc.), one of the biggest hurdles is physically mining and processing materials in the extreme and unstructured environment of space. It is like nothing we encounter on Earth.

When it comes to physically collecting samples, especially in quantities beyond those needed for scientific analysis, designers face several challenges. Some risks such as proximity operations, landing, and traversing are easier to understand and model, and hence these risks could be quantified. However, mining and mineral processing in a vacuum — and especially in a reduced gravity environment — is difficult to imagine in the abstract. At this point, many risks are simply not addressable with the amount of information we currently have. In addition, the kinds of materials to be gathered, their location, the volumes needed, on-site processing required and the end use of the materials must all be clearly defined in order to develop the mining strategy and the overall system architecture.

Current materials acquisition and analysis systems are best suited to sampling and surveying objects of interest. Any viable mining mission relies on a comprehensive understanding of the chemical makeup and physical properties of the site early in the process. After gaining a technical understanding of the composition and material properties of the resource lode, larger-scale mining efforts could begin with spacecraft tailored to the given material. In terms of supporting settlement on the Moon or Mars, this would entail special tools and autonomous vehicles designed to gather regolith, ice deposits or other materials useful to habitation. Many astroid mining programs seek to extract useful resources (such as precious metals) and return them to Earth, and keep other resources (such as oxygen, hydrogen and water) in space to power future human exploration.

Given the complexity and constraints of drilling and mining in progressively more extreme and difficult-to-reach environments here on Earth, mining in space is challenging but realistic. And if we ever want to get serious about human exploration and colonization of the solar system, there’s no alternative to gathering local resources for fuel, water and construction materials. In this respect, space mining may be the ultimate high-risk, high-reward robotic initiative.

We’re not starting from scratch. Much of this technology is based on sample collection and analysis work that the space community, and Honeybee Robotics, has been working on for decades. Over the past 25 years, Honeybee has built over fifty sample acquisition, excavation, mining and processing systems; we’ve made ten of these devoted to acquiring cores for analysis, such as envisioned for the Mars 2020 mission. The following are a few examples that represent a breadth of unique space mining technologies.

Sample acquisition and caching concepts for Mars 2020 Mission

Current plans for NASA’s Mars 2020 mission are based on the Mars Science Laboratory landing system and vehicle platform, with enhanced tools and capabilities. Mars sample return is one capability NASA is exploring. Here, Honeybee demonstrates its expertise in planetary drilling, core sample acquisition, and sample caching with an architecture for a sample return mission.

Icebreaker / LITA Drill

The 1-meter class Mars drill, LITA, during field testing on Carnegie Mellon’s Zoe rover.

Honeybee’s next-generation space drill, LITA, is designed to reach more than 1 meter deep into Mars, Europa or other bodies in the solar system. The drill has performed successfully under Mars pressures in analogous environments, including the Arctic and in Antarctica. Here, it’s shown deployed on the Zoe rover built by David Wettergreen’s team at Carnegie Mellon University, where it is gathering subsurface samples from the Atacama Desert in Chile to test for signs of life.

Auto-Gopher Deep Drill

The Auto-Gopher drill, designed for deep planetary drilling from a small platform.

The Auto-Gopher drill is a wire-line rotary-hammer drill, whose depth of penetration is limited only by the length of the wire that suspends it. This enables drilling at great depths, beyond the reach of traditional or telescoping drill architectures. The drill has been successfully field tested in gypsum, which offers conditions similar to that of water-ice at low temperature. A 2012 field test showed the drill capable of capturing 32 cores down to a depth of 3 m, with cores pulled every 10 cm, over the course of 10 hours. Integration with in-drill sensors would enable in-situ analysis of deep subsurface geological and environmental characteristics.

Asteroid Water Extractor

The concept Asteroid Water Extractor, designed for reconnaissance and resource collection.

The Asteroid Water Extractor system concept gathers water from asteroids within tight mass and power constraints, making it an ideal system for early in-situ resource extraction in support of human spaceflight. The configuration of drills located on each lander leg adds redundancy and helps maintain down-force in a microgravity environment. Water collected from the icy dirt is removed and stored for later use.

Pneumatic Miner

A functional prototype of the pneumatic mining system in Honeybee’s vacuum chamber.

Pneumatic excavation is an emerging technology that uses gas to mine and transport regolith. Initial test results have shown that one gram of pressurized gas can loft almost 6,000 grams of soil under lunar-like conditions (low gravity and in a vacuum). The excavation gas can be carried in liquid form in a designated tank, generated on site by burning residual propellant in the lander, or even generated as a byproduct of the ISRU reaction process. Because the pneumatic system has no moving parts, it is ideally suited to the lunar environment, where pervasive and highly abrasive dust introduces opportunities for mechanical failure.

Percussive Excavator

Modular percussive excavation technology reduces down-force needed by a factor of 40.

Because many mining targets, such as the Moon and Mars, have just a fraction of gravity compared to the Earth, mining and excavation systems must not rely on large down force. Percussive excavation uses vibration or hammer action to reduce required digging forces by a factor of 40. This in turn reduces the mass requirement of the lunar excavator. Because every kilogram of Lunar mission payload adds approximately $100,000 to mission cost, this technology offers billions of dollars in savings. This is a flexible, modular technology that can be mounted on a variety of handheld tools or autonomous vehicles for maximum operational flexibility.

Researchers and entrepreneurs are used to weighing the potential risks and rewards of any project they are thinking of putting time and money into. Potential financial investors — be they government policy advisors, members of grant committees, venture capitalists or angel investors — are no different. But in terms of viability, some projects, visionary and game-changing as they may be, are far from certain. Even if overwhelming technical hurdles are navigated successfully, there always remains the risk that the market will not be ready …

… and if even if the technology makes it successfully to market, leaders in innovation face potential legal and policy risks as their products enter a social arena that may be unprepared for the disruption. And yet it is the very challenge of overcoming the odds, and the thrill of achieving the near-impossible — of turning science fiction into science reality — that drives leaders and visionaries to take bold risks.

It has been well-observed that the field of robotics is at a tipping point, but many questions remain. Which of the promised game-changing technologies will succeed? Which will fail? How to best support these emerging, visionary initiatives? When is the best time to invest? And, perhaps most poignantly, can we afford to be left behind?

Over the next few weeks, Robohub will host a special focus on high-risk / high-reward robotics that will feature original articles and interviews from leading experts robotics and funding. A new article will be released every other day or so, so look out for our new Focus logo if you want to follow the series!

Our goal is to explore the potential benefits and risks of frontier robotics research, and the factors that lead to its ultimate success or failure. The series will look at the financial, legal, human, and business risks — and potential rewards — of some of the most innovative and visionary robotics initiatives currently under development. This will be complemented by a look at the various funding models (including venture capital, government grants and crowd-funding) available to high risk/ high reward researchers, and the effect of policy, law, and other social factors on a society’s ability to innovate.

We hope you enjoy this focus series … feedback is welcome, so send us your comments!