[robotics-worldwide] CFP: Workshop on Inverse Optimal Control & Robot Learning from Demonstration

Brian Ziebart bziebart at uic.edu
Thu Apr 18 15:38:46 PDT 2013

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., tele­operation, 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.

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