RL 2013 - ICML 2013 Workshop on Robot Learning

What new challenges does Robotics bring to Machine Learning, and how can existing Machine Learning techniques be used to advance the state of the art in Robotics? These are the questions this full day workshop will explore --- we hope to bring together researchers from both communities for a day of inspiring talks and heated discussion.
Although robots have become better and better at performing tasks autonomously, they are still deficient in crucial ways, especially when it comes to interacting and collaborating with their users. We believe that machine learning can contribute significantly to bridging the gap between the robots and their users. We welcome contributions on improving robot capabilities that enable robots to seamlessly interact and collaborate with humans. We encourage submissions on related topics, including but not limited to:
o Imitation Learning / Learning from Demonstration
o Reinforcement Learning / Learning from Experience
o Intent Prediction and Intnt Expression
o Activity Recognition
o Behavior and Motion Optimization
o Game Theoretic Approaches for Collaboration
o Natural (Language) Interfaces
o Adaptation and Customization
SUBMISSIONS:
Submitted papers should be up to 6 pages long (extended abstracts and preliminary result papers are welcome) and should follow the ICML format.
Each paper will be assigned two reviewers, who will evaluate the submission based on significance, relevance to the topic, technical quality and quality of evaluation.
Email your submissions to icml13workshop-AT-gmail.com.
ORGANIZERS:
o Anca Dragan (Carnegie Mellon University)
o Baris Akgun (Georgia Institute of Technology)
o Brian Ziebart (University of Illinois at Chicago)