Dr. Ludovic Righetti
Max-Planck Institute for Intelligent Systems
Tuesday, 2016-11-08 16:00
IST-Seminar-Room V9.22 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
What are the algorithmic principles that would allow a robot to run through a rocky terrain,
lift a couch while reaching for an object that rolled under it or manipulate a screwdriver while
balancing on top of a ladder? Our research tries to answer this seemingly naive question, which in
fact resorts to under-standing the fundamental principles of robotic locomotion and manipulation.
One important aspect of our work focuses on the optimal exploitation of contact interactions
between the robot and its environment to create more robust and efficient behaviors in uncertain
and constantly changing environments.
In this presentation, I will present our recent research results on the optimal control of contact inter-actions for robotic locomotion and manipulation. In particular, I will show how optimization techniques can be used in fast control loops to create complex balancing and walking behaviors. Then I will present our recent work on the optimal control of contact forces and robot motions where we exploit the structure of the robot dynamics to develop computationally efficient algorithms. I will use these examples to argue that the structure of the optimal control problems related to multi-contact legged locomotion can and should be exploited to create more efficient numerical solvers that allow receding horizon control with manageable computational complexity. In the second part of the talk, I will present some of our research on learning control. In particular, I will show how the use of multi-modal sensory information can complement optimal control approaches to create more reactive behaviors and to learn contact interactions using reinforcement learning.
Ludovic Righetti leads the Movement Generation and Control group at the Max-Planck Institute for Intelligent Systems (Tübingen, Germany) since September 2012 and holds a W2 group leader position since October 2015. Before, he was a postdoctoral fellow at the University of Southern California (2009-2012). He studied at the Ecole Polytechnique Fédérale de Lausanne (Switzerland) where he received a diploma in Computer Science in 2004 and a Doctorate in Science in 2008. He has received a few awards, most notably the 2010 Georges Giralt PhD Award given by the European Robotics Research Network (EURON) for the best robotics thesis in Europe, the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Best Paper Award, the 2016 IEEE Robotics and Automation Society Early Career Award and the 2016 Heinz Maier-Leibnitz Prize from the German Research Foundation. His research focuses on the planning and control of movements for autonomous robotic locomotion and manipulation with larger interests at the intersection between automatic control, optimization, applied dynamical systems and machine learning. Website: http://motiongroup.is.tuebingen.mpg.de/