AIC: Robots Learning Through Interaction
Thursday, May 25, 2023, 4:00pm
The RWTH AI Center has launched a new series of events: the "Artificial Intelligence Colloquium", or in short: AIC. Renowned scientists from RWTH and other universities will present cutting-edge research on methods and applications of artificial intelligence.
On 25. May, Dr. Jens Kober will be presenting on "Robots Learning Through Interaction" (in English).
The event will be held in a hybrid mode. The talk will take place in SuperC, Generali Hall and will be live streamed. After the talk, we’d like to invite you to join us for some drinks at the networking event infront of the lecture room. You will be sent the link for the live stream after the registration.
Abstract:
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Complexity arises from interactions with their environment and humans, dealing with high-dimensional input data, non-linear dynamics in general and contacts in particular, multiple reference frames, and variability in objects, environments, tasks, and human behavior. A human teacher is always involved in the learning process, either directly (providing data) 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? In this talk I’ll argue that there are tremendous benefits in having a human teacher intermittently interact with a robot also while it is learning. I will discuss various methods we have developed in the fields of supervised learning, imitation learning, reinforcement learning, and interactive learning. All these concepts will be illustrated with benchmark tasks and real robot experiments ranging from fun (ball-in-a-cup) to more applied (retail environments).
Bio:
Jens Kober is an associate professor at the TU Delft, Netherlands. He worked as a postdoctoral scholar jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He graduated in 2012 with a PhD Degree in Engineering from TU Darmstadt and the MPI for Intelligent Systems. For his research he received the annually awarded Georges Giralt PhD Award for the best PhD thesis in robotics in Europe, the 2018 IEEE RAS Early Academic Career Award, the 2022 RSS Early Career Award, and has received an ERC Starting grant. His research interests include motor skill learning, (deep) reinforcement learning, imitation learning, interactive learning, and machine learning for control.