In this 4th interview of our four-part ECHORD series, conducted last June, Sascha Griffiths from TUM talks to Raffaello D’Andrea, Professor of Dynamic Systems and Control at ETH Zurich and technical co-founder of Kiva Systems. The series explores success stories and common obstacles in industry-academia collaborations in the field of robotics, and examines the differences between these collaborations in the US, Europe and Asia.
Can you describe your background a little? I am especially interested in your transitions between industry and academia.
After getting my PhD at Caltech I became an assistant professor at Cornell University, where I was for ten years. I started the RoboCup program there and we had a RoboCup team that competed there for five years. We won the world championship four times. My area of research is dynamics and control systems, so, for me, robotics was kind of an application area. It’s a good system design problem.
While on sabbatical at MIT in 2003, I met Mick Mountz, an entrepreneur, and he told me about the problem he was trying to solve: distribution. That is when I decided to quit my sabbatical at MIT and join forces with him and Pete Wurman to start Kiva Systems.
In 2008, I officially moved to ETH Zurich but remained on as an advisor at Kiva, and I’ve been here since.
Would you say that Kiva Systems stemmed directly from your research?
No, I wouldn’t say directly. We did fundamental research on controlling robots and controlling dynamical systems. Kiva Systems has a connection to that, but it is not as though my research directly led to Kiva Systems. It was more that the knowledge, experience, and architectures that I learned during RoboCup transitioned into Kiva Systems.
Prof. Asada also mentioned in our interview with him that he thinks the practical character of RoboCup is really important. Do you think that is a good starting point?
Yes, absolutely. A lot of our first hires at Kiva Systems were former RoboCup students from Cornell University. They learned great skills while they were doing these types of competitions: how to work in teams and how to work at mechanical, electrical and computer science level issues. There are tight deadlines. You want to get the job done. There are a lot of parallels between doing things in competitions and being successful in the marketplace, and I think that is a great preparation.
Have you been in involved in academia-industry collaborations?
No, in fact I probably have a different view than most people on this topic, especially from most people in Europe. My thinking has been shaped more by the American system, where academia’s focus is on fundamental research. I would like to see more effort to strengthen academia and industry and I think the best way to do it is through entrepreneurs.
What do you think academia can offer to industry in a collaborative project? Do you think there is a reason why collaborations need to take place?
I do. I know a lot of colleagues who work really well with industry. Industry is full of interesting research problems, and it is full of folks who are interested in creating short-term, medium-term, and maybe even long-term gains. So, for them it makes sense to partner with industry. That is one way to do it. I tend to favor the other approach, which is to do fundamental research, and if it is good work, then attract the interest of entrepreneurs who are willing to learn about the new technology. There are also technologists who have an entrepreneurial spirit, who team up with domain experts and figure out how the technology could be used.
Where do you see the hurdles when technology is transferred from academia to the industry? Why do you think the transfer process is so slow?
The reward system in academia is very different to the reward system in industry. In academia, especially if you are doing fundamental research, robustness of what you are doing is not really the primary concern. It is new algorithms, new designs … and you don’t have to worry too much if it doesn’t work all the time. Of course in industry, especially with automation, that is a huge criterion. Systems have to work under different conditions; and they have to work all the time, 24/7. A lot of the time, it is about figuring out what is just a lab demo versus what is ready to be deployed in industry.
How do you think one could speed up the process of getting from the demo to a product that is commercially viable and achieving the robustness you mentioned?
Investment. Having entrepreneurs who understand where the technology is at and what is required to commercialize it, and who seek investment to actually do that process. This investment could come from private money or it could come from public funds, but it’s investment that speeds up the process. I would favor that mechanism more than just giving money to the industry and academia for collaboration and say “commercialize this technology”. If you have entrepreneurs to lubricate this process, it will speed it up a lot.
You have already mentioned the reward systems. When you think of academic and industrial partners collaborating, how can they measure the success of their collaboration in your opinion?
I think one thing is patents, which are rewarded both in industry and academia. But I also think that a big metric of success is the students that are trained. A lot of the time, if it is a real collaboration between academia and the industry, those folks will transition into industry. I think you have to take a longer-term view than just a few years. You have to understand that if there is a relationship being built, it can form a pipeline of really talented PhDs with skills in that particular area, who then often go to work for that company. That is a huge plus, which is not immediate, but will have its benefits in the medium term.
How would you measure technological progress?
That depends on which way you look at it. If you are viewing it from an academic perspective, again, the reward is if it is getting accepted in top peer-reviewed publications. From the industrial side, the question is whether it is a product. And there is some stuff in between. I think you have to look at it on a case-by-case basis. I don’t think there is a simple metric. You have to look at the technology: how many years will it take to be commercialized? What impact will it have? Will something else replace it?
Do you know of something at the moment, for example, in the research with quadcopters, where you think that it has really improved the technology?
I think the limiting factor is not the technology, it is good business models. That is what is missing. The technology is quite far along … I think that once people figure out what the right business models are to commercialize this technology, things will move very quickly.
Do you see different models of technology transfer in North America and Europe?
I know Switzerland and I know the United States, and I think they are similar in many ways. If you develop technology at ETH, and it is done during research that is being funded, the inventors get a fraction of it, and the university gets a fraction of it. If students want to commercialize a technology themselves, they can license the technology from the university. If you personally don’t, the university can license it on your behalf. They get a certain fraction of the profits, the inventors get a certain fraction… That is also how it is done in the United States. I think that works well. It has the right incentives; it is not too onerous, where the university claims everything, which disincentives the inventors from doing it because they do not get any benefit. On the other hand, it is only fair that the university should get some rewards for developing technology and it is really leaving it up to the entrepreneurs to take these ideas and commercialize them. I think in that way it is similar. I cannot really talk about the rest of Europe.
Do you see any current trends in robotics, where you think real progress is being made towards commercialization?
I think people are looking at consumer robotics. But I think in the short term, it is going to be traditional manufacturing, helping existing businesses improve their operation through automation and robotics. That is where the potential is, simply because it is still a semi-structured environment. If you sell something to a consumer, it has to work in so many different situations. Every home is completely different. Whereas, if you are selling to a business that is trying to improve their manufacturing capabilities, for example, they have leeway on changing the way things are done, rearranging things, instrumenting things to help their operations. So, that is where I think you will see the big in-roads in robotics and automation.
Are we currently in a situation of increasing the market share and selling more, or are we seeing real progress?
No, I don’t think the market size is increasing. Before, it did not make sense to use automation in certain areas, but now the technology is improving in such a way that it does make sense to do so, and to do so in more industries.
Do you think academics lose interest as soon as something reaches market readiness?
Yes, I think that is the case. Academics are interested in doing fundamental research, and there is a different set of values and constraints when you want to commercialize something. For example, robustness – which I think is actually an interesting topic itself – has a lot of interesting research problems associated with it. They tend to be more at the system level. And I think only a few people are really interested in doing those sorts of things. So, I think, for the most part, academics lose interest. Not all, but a fair amount do.
Do you think that is a good or a bad thing?
I do not think it is a bad thing. I think that, again, you need something in between university research and commercialization to facilitate this process. There are many ways to do that. In Germany and in a lot of other European countries, there are strong industry-university collaborations. In the United States, it is more the entrepreneurial folks that are forming the glue. I tend to like the US model best. I think that it enables the quickest transition. Entrepreneurs are the ones who take on the stuff that is not ready for prime time, so to speak, and actually do it. And it takes investment. So, it is not bad that this happens … because without it, you would not get fundamental research done. So, I think that is perfectly fine.
In your TED talk, you talked about science, engineering, and arts coming together. I think you did not mention business and industry as such. Do you see these worlds connecting well or do you think they are all individual communities that tend to not communicate?
I think that they are separate entities, but there are people who are always at the overlap. You need enough of these people to ensure that there is a transfer of knowledge and interests.
Do you think the business, engineering, and science communities communicate enough?
I think they do. They could do more, of course. And I think the right way to do it is to provide the right incentives.
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