Intuitive Surgical offers funding for technology research grants for projects in 2016. Researchers at non-profit institutions world wide can apply. LOIs can be submitted until June 5.

Overview: The purpose of these grants is to support technology research in the field of surgical robotics, or related fields. Successful proposals will address clinically-relevant technology development. Grants will be awarded to researchers at non-profit academic institutions worldwide. Awards will be conferred on a competitive basis by submission of a grant application.

Available Grants: Applicants may apply for $10,000 to $50,000 of funding per project (US Dollars, total costs). The grant monies can be used to fund salaries, equipment, supplies and/or travel required to support the proposed research project for a period of up to one year.

Selection Process:  Applicants must first submit a Letter of Intent to propose (LOI), as detailed below. Grant LOIs will be reviewed and ranked by an internal selection committee. Those pre-proposals meeting a predetermined scoring threshold will be invited to submit a full grant application (not exceeding 10 pages, excluding bibliography and references). All invited full grant applications will be reviewed and scored based on the following criteria:

* Clarity of research goals and objectives.
* Novelty and innovation.
* Clinical relevance and value.
* Evidence of feasibility.
* Strength of evaluation methods and measures.
* Availability of required resources.

Letters of intent and full grant proposals will be handled in a confidential manner and will not be distributed beyond the grant selection committee at Intuitive Surgical. Due to the expected volume of proposals, we regret that the committee will not be able to provide detailed individualized feedback for unsuccessful proposals.

Letter of Intent Guidelines: A two-page letter of intent to propose should be submitted by all grant applicants. Submissions will consist of 5 parts:

* Summary: one or two sentence proposal overview.
* Description of Problem/Background.
* Purpose, Hypothesis and Methods of research (not more than 500 words) for the project.
* Capabilities: Indicate the identities, capabilities and credentials of investigators as well as any participating institution. Include contact information for applicant principle investigator.
* Budget: Include a rough project budget that includes estimated costs for staff, equipment, consumables, and other direct costs. Applicants can request up to $50,000 in total costs, with no more than 20% of this total constituting indirect costs.

Click here to submit a letter of intent on-line for the Technology Research Grant.

Deadlines:

* We are asking interested applicants to submit a Letter of Intent by June 5, 2015. Invitations to submit full grant applications will be sent by July 2, 2015 (this invite will include proposal guidelines and forms).
* The deadline for submitting a full, invited grant application is September 11, 2015. Only complete application packets will be considered. Awardees will be notified by October 16, 2015. Grant monies must be used to support research expenses during the 2016 calendar year.

Grant Conditions:

* Intuitive strongly encourages institutions who have not previously received funding from the company to apply.
* Multiple grants will not be funded simultaneously to any Principal Investigator.
* Multiple grants to the same institution for different principal investigators will be considered.
* Awards must be used to fund research at academic institutions. Grants are not to be used to fund for-profit activities.
* Grant monies must be used to support research expenses during the 2016 calendar year only.
* Total costs may include no more than 20% indirect costs and the total budget, including both direct costs, indirect costs and any applicable taxes, should not exceed $50,000 USD.
* In the case of technology development, IP terms and conditions may require a negotiated agreement.
* Grantees will be invited to present their work at our headquarters in Sunnyvale, CA (travel costs covered by Intuitive Surgical).
* All applicants selected to receive a grant are required to submit a one-page mid-year progress report, as well as a final status report that details study results and conclusions. If the funded project does not take place, all monies awarded will be refunded to Intuitive Surgical. If the project is terminated early then a partial refund, with an accounting of the funds expended up to the termination date, will be provided by the Principal Investigator. Any unused funds as of 31 December 2016 will be refunded to Intuitive Surgical.

To submit a letter of intent, use our on-line application process 
Click here to submit a letter of intent on-line for the Technology Research Grant.

Please email grants@intusurg.com, with questions regarding the grant process. If you do not have email access, please send a hardcopy of your letter of intent to:

Dale Bergman
Research Coordinator
Intuitive Surgical, Inc.
1266 Kifer Road
Sunnyvale, CA,
U.S.A.  94086

– See more at: http://intuitivesurgical.com/company/educational-grants/technology_grants.html#sthash.CjqcBHOH.dpuf

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.

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!

This month we asked the Robotics by Invitation panel to tell us what kind of research they would undertake if money weren’t an obstacle. Here’s what Mark Tilden and Illah Nourbakhsh have to say …

Illah Nourbaksh on “What would you research if you did not have to worry about grants?”

 Community empowerment through massive robotic sensing. There is no question we live in a world that is changing. Pollutants are changing the dynamics of the air we breathe, the water we drink and even the soil on which we live. Yet the power to measure pollution …

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Mark Tilden on “What would you research if you did not have to worry about grants?”

 Well, I’m lucky enough to be a gentleman scientist, so I concurrently study problems on minimal dynamical control systems (optimizing performance to silicon ratios), power regeneration and efficiency, alien robot morphologies …

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Y’know, the basics.  Bringing good things to life.  Moo Ha ha.

So robotics research is excellent for those with ADHD – the field’s problem and feature is it’s not just anything, it’s everything that’s techno fun. However every now and again there’s something that skitters, flops, pronks, spins, walks, tumbles, or bounces across the desk that could really use … a brain.

So the short answer is I’d put (other people’s) money into researching affordable competent minds that could help organize any mechanical body, sensor or environment they are given.  Small, quick, cheap, and with a voice interface so I can encourage it to effectiveness without a million keystrokes.  Power on and it asks “Hello, what is my name?”

Yes, that’d be handy.  Do they have an App for that yet?

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There is no question we live in a world that is changing. Pollutants are changing the dynamics of the air we breathe, the water we drink and even the soil on which we live. Yet the power to measure pollution, measure human behavior (including Emergency Room visits) and correlate the values is held tightly by government and corporate players.  They have the money to focus on sensors and values that make their case, and they have the marketing skills to then present those values in the best possible light for reelection and for corporate profit.

But in fact those most touched by a changing world are ordinary citizens, and it is the citizen who has the potential to make decisions that immediately impact health and future legislation, from what neighborhood to live in to which politician to elect. Robotic sensing technologies are rapidly becoming less expensive, and with the right infusion of research I believe we could develop the networking, data visualization and interaction smarts to have global, publicly accessible information about all sources of pollution. This would empower citizens and communities to make far more informed decisions, and to fight biased information presentations with their own re-interpretation of source data. This will take new innovation in sensing technologies, networking, Big Data storage, search, retrieval and evaluation.

It is the stuff of robotics, through and through, applied to the deep goal of community empowerment at an international scale.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(1) strategic funding from NASA, DARPA, DoD, NSF and other government organizations to do the pure science involved in solving stumbling blocks in robotics, and

(2) entrepreneurial-initiated funding from friends and family, angel investors, VCs and “special people” like Scott Hassan, Jeff Bezos, Larry Page, or Sergey Brin.

In addition, some funding is available via macro programs such as the Roadmap for US Robotics, which don’t move at the same speed as the entrepreneur-initiated projects.

Finally, surrounding each of the major universities involved in robotics research and education are clusters of support networks working with and supplementing the universities’ own commercialization activities. Stanford and UC Berkeley in the Bay Area of California; Georgia Tech in Atlanta; CMU in Pittsburgh; and MIT and Harvard in Boston. All of these clusters and commercialization activities are without government stimulus or direction.

Willow Garage is a perfect example of the benefits of special people: Scott Hassan had a vision to jump-start robotics – particularly the open source software side – and he invested hundreds of millions of dollars in that pursuit. From Willow Garage came seven notable spin-offs including an ongoing non-profit to perpetuate ROS.

Sergey Brin and Larry Page from Google established Google X-Labs, invested in Tesla Motors, and many more.

Amazon’s Jeff Bezos has set up a fund that has invested in all sorts of start-ups: from Uber to Behance to Linden Labs.

The real excitement comes from the special people: They not only enthusiastically give back with profits from their own experiences, but also bring the same level of energy that made them successful to funding of new robotics projects.

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1. Continue to fund robotics competitions.  Not only is this a great way to educate researchers on how to build real systems, but the approaches adopted by the winning teams are highly correlated to what is feasible with today’s technology. Also, the challenges these teams encounter in a one-year competition cycle are similar to those faced by a young startup in its first year of existence. This funding approach could be expanded to include team projects that create large-scale public installations because, like startups, they too must be reliable and robust to succeed in a public, real-world context.  Finally, one must also make sure that entrepreneurs know about these competitions and team projects, and that they have the right incentives to attend.
2. Provide funding that allows freshly-minted PhDs to transition from fundamental research to applied research with a specific business focus, and at the same time provide support from business mentors and entrepreneurs.  A pre-startup phase, if you will. This not only brings the research closer to application, but it also gives individuals an appreciation of what it takes to commercialize their research, without the immediacy and constraints of being a startup.  A good example of this kind of funding scheme is the Pioneer Fellowships program established at ETH Zurich.

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