||Exploiting Variable Impedance for Robotics: Mimic or Optimize?
||Wed 7 Mar 2012
||Earl Mountbatten Building Rm 2.33
||Prof. Sethu Vijayakumar, University of Edinburgh
||It is the year of the London Olympics and appropriately, this talk is about making robots run faster, jump higher and throw further. Variable Impedance refers to the ability to change stiffness and damping during task execution. With novel prototype robotic actuators capable of fast impedance modulation, the obvious question is how we can maximally exploit this capability in an automatic and data driven manner? In this talk, I will look at impedance modulation in three different classes of movements: point-to-point tasks like reaching, explosive movement tasks like throwing and rhythmic movement tasks such as walking and running. I will describe an optimal control based formulation of optimizing both the temporal profile of movement and impedance modulation in a way that is tuned to the dynamics of the plant. Several hardware tests will serve to highlight the benefits. Further, I will illustrates the pitfalls of naively mimicking impedance profiles across heterogeneous systems (e.g., human limb to VS joints or MACCEPA actuators) and describe a framework that is capable of abstracting out the specific plant dynamics while ensuring task optimality. This talk will draw upon concepts of optimal feedback control, apprenticeship learning and model free reinforcement learning besides fundamentals of dynamics representation and learning.
Short Bio: Sethu Vijayakumar is Professor of Robotics and the Director of the Institute for Perception, Action and Behavior (IPAB) at the School of Informatics at the University of Edinburgh, UK. Since August 2007, he holds a Senior Research Fellowship of the Royal Academy of Engineering, co-funded by Microsoft Research. He also holds additional appointments as an Adjunct Faculty at the University of Southern California (USC) and as a Visiting Research Scientist at the RIKEN Brain Science Institute, Japan. His research interest spans a broad interdisciplinary curriculum involving basic research in the fields of statistical machine learning, robotics , sensorimotor control, and computational neuroscience. Prof. Vijayakumar has pioneered the use of large scale machine learning techniques in the real time control of large degree of freedom anthropomorphic robotic systems including the SARCOS and the HONDA ASIMO humanoid robots, KUKA-DLR robot arm and Nao mini-humanoids. He is the author of over 120 peer reviewed publications in these fields, the winner of the IEEE Vincent Bendix award, the Japanese Monbusho fellowship besides serving on numerous EU and NSF grant review panels and program committees of leading machine learning and robotics conferences.
<< Back to list of seminars