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 understanding 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 particular, I will show recent results on legged robot control, where we use optimization techniques for real-time control of both contact interactions and robot motion. I will show experimental results on a torque controlled humanoid robot and recent trajectory optimization techniques to efficiently plan motion together with interaction forces during locomotion. The second part of the talk will focus on compliant manipulation. We developed complete systems capable of achieving complex autonomous manipulation tasks and I will show how multi-modal sensory information can be exploited to create very reactive behaviors and to learn contact interactions using machine learning techniques.
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 Computational Learning and Motor Control Lab (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 (eq. MSc) 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 and the 2016 IEEE Robotics and Automation Society Early Career Award. His research focuses on the planning and control of movements for autonomous robots, with a special emphasis on legged locomotion and manipulation.