8 quotes found
"So this is the interesting part, is the robots at the time were mostly hydraulic robots. Very large robot, heavy robots. There was a robot from Renault that I think it was called the R80. Huge. Eighty kilograms it can carry. And I really didn’t implement my approach on that robot, but I used this cable-driven robot called the MA23 manipulator that was designed and developed by Jean Vertut. Jean Vertut is probably the father of robotics, robot design, in France. And there is a number of very, very influential people in my early career in robotics, and Jean Vertut was one of them. Jean Vertut developed most of the concept that led to cable-driven actuation. The three-fingered hand Kenneth Salisbury developed was based on a lot of those concepts. Many of the robots that were later developed were based on ideas and concepts that Jean Vertut developed. Jean Vertut passed away very quickly years back, and the whole robotics field missed him. But I had this privilege to know him. And also the privilege of having my paper selected by him, my first paper, to be then – the introduction to me to know Professor Roth, who eventually invited me too to come to Stanford."
"So I became involved during these years in one of the early project in robotics called Spartacus. Spartacus is a project to develop a robotic system that is aimed to assist human. And in this project a number of research groups around France, from the South to the North, in were involved in that pioneering project. I think this was probably one of the earliest project in Europe. And it brought a number of people who are still today involved in robotics. So if we go over the group of people who is today involved in robotics we will find many of them who participated in that project. However, my research itself was very closely connected with another laboratory in France. Because of my work was pursued at the school SUPAERO. Next to SUPAERO there is a very important center of robotics, and this is called LAAS, L-A-A-S. And at LAAS, there was a very important project initiated and led by Georges Giralt. And Georges Giralt was in fact one of my other mentors. In fact, he was the director of my post-doctoral study. He was the chair of the committee of my defense, and with Georges we had a long, long years of interaction, sharing a lot of the ideas that in fact became part of my own research or the research that has been pursued over the years at LAAS. Georges Giralt was leading the robotics research effort at LAAS, and one of the researchers there is Mark Runeau. Mark Runeau was one of my very close colleague, because both of us, we were interested in dynamics. And Mark Runeau, together with Wisama Khalil from Montpellier, were also, the three of us, we were working in different aspect of dynamics. In fact, Wisama and Runeau developed software to generate automatically, symbolically, the dynamic equation of robots, that in fact I used myself in developing some of the dynamic models. But my approach to dynamics, as I said, was based on the idea that we need to understand dynamics at the level of the task. Not just at the level of the joints. So I was doing this transformation and I had a great partner with Mark Runeau for discussion. And Mark Runeau was very close to me in Toulouse. And we shared a lot of moment of discussion on dynamics."
"So arriving to Stanford, it was wonderful. We had the AI lab in Margaret Jacks. In the basement of Margaret Jacks, next to the DEC-20. We had a small room with one table and four robots. The AI lab just moved from the hills down to Stanford to the basement of Margaret Jacks. At that time, there were a number of, I mean, extraordinary people that were involved in the AI lab. The AI lab attracted a lot of talent in robotics over the years. Just behind me you can see the Vic Scheinman robot, the Stanford Scheinman arm robot. There were a number of people who were involved in the ‘70s in the AI lab. In the ‘80s when I arrived, we had John Craig was there. Tom Binford was leading the AI lab. My friend, the very famous researcher in both AI and robotics, Rodney Brooks, was there. In fact, many other people came through as visitors. We had also a very famous designer, Ken Salisbury, who was there. There were a number of people from the field of vision. There was Harlan Baker. I don’t want to go over all the names. I have the list and I have their photos. If you would like, we will look at that later. But it was an amazing dynamic environment that I’ve never experienced in my life. It was just the discovery of that exciting place that made me decide to stay one more year and one more year. Then Stanford just kept me forever, because it is very, very difficult to leave this environment. The research environment, the excitement, the opportunities to take on a new project, is really unique. What is interesting also is the fact that while I was at Stanford, I was in France as well, collaborating with my colleagues. I was in fact involved with other collaborations in Japan. In the year ’84, ’83, and in fact December ’83, I visited Japan for the first time, and I had the great, great opportunity of meeting Professor Inoue, Professor Umetani, Hirose, Professor Nakamura, who was a student at the time. And his advisor, Professor Hanafusa, and Professor Yoshikawa, and also a number of, I mean, just pioneers in robotics. All of that was done during one trip I had to Japan in 1983. Since then I kept in touch with all these different laboratories over the years, and that was really an amazing experience. What I discovered about Stanford and about the AI lab is the fact that what we are doing is so much connected to the world, and that became one of the other dimension for my research."
"Well, this is really interesting, an interesting question. Looking back at my Ph.D. where I started, looking at the vision I had in that work, I feel like I’m still working on my Ph.D. That is from the beginning, I took on a challenge, which is to try to understand the robot system with respect to descriptions of the task. That is, to try to not just worry about modeling the physical robot in a convenient space, which is essentially the joint space. I was interested in understanding the characteristics of the robot when it’s interacting at a given point with the environment. What would be the effective masses that I will feel here when I’m going to touch the environment? What is the normal space in which I can exert forces and what would be the corresponding tangent space where I can move? A lot of questions related to the dynamics and the control of robots in task space have not been really fully addressed. There are few people around the world who really look at those problem. Neville Hogan thought about it in the context of analyzing human motion and proposed very interesting models using impedance control. There are a number of other researchers who looked at those problems, but synthesis in task space has not been something that a lot of work was done in, and that was really one of the things that I was committed to pursue and to work on. So there was that work that was taking me from thinking about how, “Can we control just one effector?” Then how can we go from one effector that is not only moving in free space, but obviously interacting with the environment? But then how can we go from one robot end effector to two end effectors? How can we do comparative manipulation when we start to have internal forces? Then how can we have general cooperation between multiple robots? Then how can we make this robot mobile manipulators? Just behind me you can see Romeo and on this side you have Juliet. These are the two platforms that we developed to explore the workspace of a human. That is, we always thought about these manipulators attached to a table and bringing the component to be assembled by those robots."
"Now, if we are really to think about robots that would interact with human or move in the human environment, we would need to have those robots not fixed but mobile. So we started to look at mobile manipulation, and that brings a lot of interesting issues in the dynamic of macro mini-structures. The properties of dynamics in relation to reduce effective inertias, redundancy, and all kind of things that were amazingly interesting to analyze and work on. As we pursued this work, we discovered that, “Well, there’s more.” When we got to humanoid robotics we have the branching, complex branching structure, with multiple tasks that need to be simultaneously achieved while maintaining constraints, while maintaining balance, given the fact that these robots are under-actuated. It’s amazing. You have one problem after the other. It’s so rich, so exciting. So that was one line of the work that I was really interested in, dynamic control and control structures."
"But there was as, you know, from the beginning I was interested also in the aspect of interaction with the environment through understanding of obstacles and the need to avoid collision and the need to create motions around those obstacles. So having resolved the problem in term of reactivity doesn’t mean that we resolved the global problem. Because motion planning requires us to think about a global path. Now, if we think about the computational complexity of this, we realize that this is exponential in the number of degrees of freedom and it is costly to find a path, to plan a path, especially for a robot with many degrees of freedom. I was always interested in finding a way to connect those planning levels, that is the level where we are planning the motion with the levels of execution where those levels are running at very high frequencies. If we think about the control, we need to have a millisecond of control , whereas if we think about a motion planner, well, between the time we find the obstacles and perceive the environment to the time we come up with the new plan, it’s going to take long time. Especially in the ‘80s. What we really tried to do was to create an intermediate level that would allow us to think about this connection between motion planning and control in a way again similar to that of human. The idea that became known as elastic planning is to start with a plan and continuously adjust this plan reactively. Locally by mechanisms really simple as repulsive potential forces, but the trajectory itself is treated as a physical entity with property of elasticity that will make it shorten and adjust to the environment and the changes in the environment. That became a very, very interesting technique that also is being pursued by a number of my current student and previous students like Oliver Brock, who worked on this. Sean Quinlan developed the early version of it, and now a number of new students are working on taking this further, not only to move in the free space, but also to think about the contact space."
"Right. I mean, the whole idea that really is pursued here is human. I mean, if we think about human, human never go through this exercise of precisely planning every aspect of their motion. We do not compute a full trajectory or a full plan. What we do is like we are sitting here, and after the interview you’re going to go back to the parking. You’re not going to plan all the details of that motion. What you know is you need to go to a door and you know that it’s going to be feasible to go to the door. Probably you have some intermediate landmark along the way. So we plan with this concept of feasible, reachable locations, and along the way we know that we are going to accommodate our motion using our reactive behavior. We are interacting with the world as we are moving. In your car you are driving around other cars and people and bicycles. It wasn’t completely in the plan. But you know that skill is there. Ee make use of skills, and what we do when we are interacting with the physical world, when we are making contact, when we are assembling objects, when we are putting things together. We are also using skills. When we are playing tennis, when we are doing any challenging task, we are using skills. So the idea is let’s then plan – first of all, let’s develop those skills for the robot. That is, skills are a more abstract, a more advanced capability for the robot than the simple control of following a trajectory or reaching a point or just touching a surface. It is a skill to say, “I’m able to accomplish this task, taking into account different things.” By building those skills, the planner can plan using those skills. So the task of planning becomes much less difficult and complex, and in real-time, the skills can perform at much faster servo rate than any dependency, full dependency, on the planner that would be required if we didn’t have the skills. So all the challenge is, “How can we teach these robots those skills?” This is what we are doing. We are making use of a lot of fundamental characteristics of those robots in term of their kinematics, dynamic models, so that we build the first layer and then we abstract that layer to create a behavior that can accomplish a different task in the environment with obstacles, with a object. So we talk about compliant motion behaviors that allow the robot, for instance, to move to a multi-contact with the environment by just detecting and feeling those contacts. You can imagine a task of just putting a lid on a box. This is something really hard, if we want to do it just by controlling the motion and the position, but actually this is something that becomes very easy if we understand compliant motion. So this becomes a skill that then could be used and reused in different situation."
"Yeah. It is really, to say, that we get a lot of inspiration from the human. But we are not trying to just record human motion and replay the motion. What we are trying to do is to really to understand what is behind this motion, what is the strategy, and how can we encode that strategy in something that the robot can use as a strategy so we can generalize? But this is, again, part of the whole approach. We are seeking solution that can address a versatile number of problems. We’re not just looking for finding or engineering the solution to a specific problem. Because robotics is about ultimately all kind of task in all kind of environment, and we really need to look at this versatility."