Connectionists: [Call for Participation] Learning for Locomotion Workshop

[Jan Peters]

=======================================================
                                          Call for Participation
                    Robotics 2005 - Workshop: Learning for Locomotion
                              Cambridge, MA, USA --- June 11, 2005
=======================================================

Quick Facts
Organizers:	Jan Peters, Russ Tedrake, Stefan Schaal
Conference:	Robotics - Science and Systems 2005
Date:	 June 11, 2005
Room:	 To be announced
Location:	 MIT, Cambridge, MA, USA
Website:	http://www-clmc.usc.edu/~jrpeters/workshop.html
Alternate Website: 
http://www.jan-peters.net/Research/LearningForLocomotion

Abstract
Over the last few decades, there has been an impressive amount of 
published work on legged locomotion, including bipedal walking, 
running, hopping, stand-ups, summersaults and much more. However, 
despite all this progress, legged locomotion research has largely been 
driven by researchers using human insight and creativity in order to 
generate locomotion control algorithms. In order to improve the 
robustness, energy efficiency, and natural appearance of legged 
locomotion, there may be a significant advantage to using machine 
learning methods to synthesize new controllers and to avoid tedious 
parameter tuning. For instance, it could be advantageous to learn 
dynamics models, kinematic models, impact models, for model-based 
control techniques. Imitation learning could be employed for the 
teaching of gaits patterns, and reinforcement learning could help 
tuning parameters of the control policies in order to improve the 
performance with respect to given cost functions. In this context, we 
would like to bring together researchers from both the legged 
locomotion and machine learning in order to discuss which locomotion 
problems require learning, and to identify the machine learning methods 
that can be used to solve them.

Goal
In order to better understand the application of machine learning 
techniques to locomotion, our goal is to bring together researchers who 
represent many different approaches to biped locomotion control with 
their peers in machine learning for control. We hope to discuss future 
research directions for principled machine learning approaches to biped 
locomotion. The workshop will address topics such as:
* Which unsolved biped locomotion problems can be solved using learning?
* Can walking be broken down into components upon which machine 
learning methods are applicable?
* What models (e.g., forward, inverse, impact) would be desirable for 
controlling locomotion?
* Can machine learning methods help solve the gait generation and 
foot-placement problems?
* Can human learning of locomotion yield insights for both robotics and 
machine learning?
* Which machine learning algorithms are suitable for online 
implementation on the robot, and which problems can be solved in 
simulation?
* What cost functions should be used to describe "optimal" walking, and 
what experiments should be done to test our controllers?
Furthermore, we intend to kick-off the Legged Robot Control Competition.

Program
The tentative program and list of speakers can be found at the 
Workshops Website ( http://www-clmc.usc.edu/~jrpeters/workshop.html ) 
or the alternative website ( 
http://www.jan-peters.net/Research/LearningForLocomotion ).

Organizers
The workshop is organized by Jan Peters, Russ Tedrake and Stefan 
Schaal, from the Departments of Computer Science and Neuroscience, 
University of Southern California, Los Angeles, CA, USA and from the 
Brain and Cognitive Sciences Department at the Massachusetts Institute 
of Technology, Cambridge, MA, USA.

Location and More Information
The most up-to-date information about Robotics - Science and Systems 
2005 can be found on the Robotics 2005 website ( 
http://www.robotics-conference.org ).