IOC 2013 - Workshop on Inverse Optimal Control & Robot Learning from Demonstration

Workshop on Inverse Optimal Control & Robot Learning from Demonstration
In conjunction with the Robotics: Science and Systems (RSS) conference on June 27th in Berlin, Germany
Homepage: http://www.cs.uic.edu/Ziebart/IOCRLfD
Important Dates:
May 10th: Submission deadline
May 17th: Author notification
June 27th: Oral/poster presentations
Submissions: send extended abstracts and papers (1-8 pages in length) to rss.ioc.rlfd.2013-AT-gmail.com
Description: In many robotic domains, it is much easier to demonstrate appropriate behavior (through e.g., tele?operation, haptic feedback, or motion capture) than it is to program a controller to produce the same behavior. Driven by this observation, research in learning from demonstration and inverse optimal control has become increasingly popular in the last several years. This paradigm recasts reinforcement learning problems as supervised learning tasks, in which advances in machine learning can enable robots to learn the desired policy, utility, and/or dynamics of the robotic domain directly and efficiently from observed behavior. For example, inverse optimal control aims at identifying the unknown objective function or policy that produces a given solution of an optimal control problem. Input data can come from measurements related to the system’s state e.g. by motion capture, IMU or force plates. The identified function can then be used to generate optimal motions for robots. An important goal of this workshop is to present and discuss the state of the art of solution methods for this challenging class of problems.
In this workshop, via a mix of invited talks, posters, and discussion, we seek to bring together experts in system identification, reinforcement learning, and inverse optimal control to explore the theoretical and applied aspects of learning from demonstration and inverse optimal control. We plan to discuss open problems, state-?of-?the-?art solution methods, and interesting applications.