Martial Hebert became director of the Robotics Institute in November 2014. A native of Chatou, France, Hebert earned his doctorate in computer science at the University of Paris and joined the Carnegie Mellon faculty in 1984. His research has focused on computer vision and perception for autonomous systems, including interpreting both 2-D and 3-D data to build models of environments.
He worked on NavLab, CMU's pioneering program for self-driving vehicles, and more recently led the development of perception capabilities for other robotic platforms.
Hebert has served on the editorial boards of the IEEE Transactions on Robotics and Automation, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and present serves as editor-in-chief of the International Journal of Computer Vision.
He spoke to Link Editor Jason Togyer.
What was your earliest computing experience?
When I was an undergraduate, I was in mathematics, but I took some classes in computer science—I started on punch cards with a Univac, I don’t remember the model—and it was interesting to see how mathematics could be put into practice. So I moved first into applied math, and then into computing.
Did you move from math to computer science because there are more open problems to solve in computer science?
There was certainly a little bit of that. Those also were the days when people were talking about artificial intelligence, and I had the opportunity to be in touch with some of the early labs working on computer vision and robotics—in fact, my advisor was Olivier Faugeras, who became one of the leading names in computer vision research. I was very fortunate to be exposed very early to this sort of thing.
It seems to me that computer vision has moved out of the lab and into people’s everyday lives. What is fueling that move from the theoretical to the practical?
Well, sensors are no longer an expensive proposition. We also now have cameras that have very small form factors and draw very little power. That’s one aspect. The other aspect is computing—by which I mean processing power and storage. And then, of course, there’s all of the progress that has been made in algorithms over the past 30 years. All of those combined make it now possible to have an explosion of commercial applications. And it had to be all three, because even if you had the math but you didn’t have the computing power or the sensors, it wouldn’t get you anywhere.
Is robotics also at a tipping point in terms of commercial applications?
The field is becoming very mature. Thanks in part to the work we’ve done here, industry is doing state-of-the-art research in robotics. That takes away from what we traditionally have done, so we have to adapt to that. It’s a normal process of a field going from purely academic research to maturing into something bigger. As it’s becoming very mature as a field, it requires a lot more skilled workforce, so there’s an opportunity for us in terms of education that is tremendous.
Does that mean growing the graduate programs?
That’s one of the aspects, adding new disciplines. For example, we’re starting a master’s degree in computer vision because computer vision is growing so rapidly. We need to continue to grow our educational programs to respond to the growing need, and we need to continue defining our areas of leadership.
One of the big issues in computer science is privacy. What privacy concerns emerge in computer vision?
We now have technology that not only can track people, but can also recognize emotions. Some of the leading systems can recognize activities and perhaps recognize anomalous behaviors. Once you get into those areas, privacy becomes a very serious concern. We’ve done some work already in those areas—we’ve worked with personal care and nursing robots, for instance, where we had to deal with very deep privacy concerns. Things get even worse when you have systems in someone’s home, and worse and worse when we work with distributed systems and storage.
How do researchers address the concerns?
Well, it’s not just a robotics solution. It’s multi-disciplinary. We concentrate on developing technologies knowing full well that when we go to use them, we’ve got to face those issues, and it takes a lot more than just our people, our work, to do that. We need people from computer science, social science, legal experts, and so forth.
What misconceptions do people have about robotics?
The media’s view of robotics is both good and bad. It’s good because it popularizes what we’re doing, but it raises expectations in directions that are not ones where we should be raising expectations. There are also bad aspects or dangerous aspects of robotics that are portrayed in the media. Perhaps we don’t take that seriously enough. We also still have a lot of work to do to educate people on the fact that robotics is not just humanoid robots. Robotic systems come in many, many different shapes and forms and appearances and have many, many different ways to interact with people.
What is the next frontier in robotics?
Dependability. One of the things that’s critical for robotic systems to be truly accepted around humans is “safety.” Not safe in the sense of “never fails,” because everything fails at some point, but safe in the sense that you are very confident that you can trust it. When you drive your car, you trust the machine. So what needs to be done to trust a robotics system? They need to be able to detect anomalies in their own behavior, they need to be able to explain and report them. That’s the level of intelligence they need. This translates, by the way, into a large set of very hard technical problems which people have been looking at in different projects, but there’s a need to combine them into a discipline and that’s going to be critical.
There is a very good chance that CMU and Astrobotic will be going to the moon in the near future. What will that mean for the Robotics Institute?
It will say something about how advanced the field is. It says that we are still addressing incredibly challenging problems with the research we do here, but at the same time, we’ve gotten to the point we can do incredibly challenging things, like putting something on the moon.
It’s exciting because in every field, there’s a curve where you have a rapid increase, because you start with nothing, but then it plateaus for a while. We’re not in the plateau. We’re in the very, very rapid increase, and that’s a very nice place to be at this point.
Jason Togyer | 412-268-8721 | email@example.com