The importance of force control for multi-contact behaviors
The development of algorithms capable of exploiting both contact interactions and robot dynamics to achieve complex tasks often relies on the assumption that robots behave as ideal torque sources. High performance torque-controlled capabilities are therefore very important to enable the use of such algorithms. In this talk, I will show our recent results on the control of both contact interactions and robot motion based on force control. I will then present our trajectory optimization techniques to efficiently plan motion together with interaction forces during locomotion. Finally, I will show experimental results on a torque controlled humanoid robot and the practical challenges in getting these algorithms to work on real systems.
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 between March 2009 and August 2012. He studied at the Ecole Polytechnique Fédérale de Lausanne (Switzerland) where he received a diploma in Computer Science (2004) and a Doctorate in Science (2008). He has received a few awards, most notably the 2010 Georges Giralt PhD Award given by the European Robotics Research Network 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.