Learning to Interact & Interacting to Learn
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Reinforcement learning and imitation learning are two different but complimentary machine learning approaches commonly used for learning motor skills.
I will discuss various learning techniques we developed that can handle complex interactions with the environment. Complexity arises from non-linear dynamics in general and contacts in particular, taking multiple reference frames into account, dealing with high-dimensional input data, interacting with humans, etc. A human teacher is always involved in the learning process, either directly (providing demonstrations) or indirectly (designing the optimization criterion), which raises the question: How to best make use of the interactions with the human teacher to render the learning process efficient and effective?
All these concepts will be illustrated with benchmark tasks and real robot experiments ranging from fun (ball-in-a-cup) to more applied (unscrewing light bulbs).
Jens Kober is an associate professor at the Cognitive Robotics department, TU Delft, The Netherlands. He worked as a postdoctoral scholar jointly at Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He received his PhD in 2012 from Technische Universität Darmstadt, Germany. From 2007 to 2012 he was working at the MPI for Intelligent Systems, Germany. He has been a visiting research student at the Advanced Telecommunication Research (ATR) Center, Japan and an intern at Disney Research Pittsburgh, USA. Jens is the recipient of the 2018 IEEE-RAS Early Academic Career Award in Robotics and Automation and the 2013 Georges Giralt PhD Award. Jens serves as co-chair of the IEEE-RAS TC Robot Learning.