Post-NIPS Workshop on Robot Learning

[David Cohn]

The following workshop will be held on Friday, December 3rd in Vail,
CO as one of the Post-NIPS workshops. To be added to a mailing list
for further information about the workshop, send electronic mail to
"robot-learning-request at psyche.mit.edu".
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NIPS*93 Workshop:    Robot Learning II:  Exploration and Continuous Domains
=================

Intended Audience:   Researchers interested in robot learning, exploration,
==================   and active learning systems in general

Organizer:           David Cohn (cohn at psyche.mit.edu)
==========           Dept. of Brain and Cognitive Sciences
                     Massachusetts Institute of Technology
                     Cambridge, MA 02139
Overview:
=========

The goal of this workshop will be to provide a forum for researchers
active in the area of robot learning and related fields.  Due to the
limited time available, we will focus on two major issues: efficient
exploration of a learner's state space, and learning in continuous
domains.

Robot learning is characterized by sensor noise, control error,
dynamically changing environments and the opportunity for learning by
experimentation.  A number of approaches, such as Q-learning, have
shown great practical utility learning under these difficult
conditions.  However, these approaches have only been proven to
converge to a solution if all states of a system are visited
infinitely often.  What has yet to be determined is whether we can
efficiently explore a state space so that we can learn without having
to visit every state an infinite number of times, and how we are to
address problems on continuous domains, where there are effectively an
infinite number of states to be visited.

This workshop is intended to serve as a followup to last year's
post-NIPS workshop on machine learning. The two problems to be
addressed this year were identified as two (of the many) crucial
issues facing robot learning.

The morning session of the workshop will consist of short
presentations discussing theoretical approaches to exploration and to
learning in continuous domains, followed by general discussion guided
by a moderator. The afternoon session will center on practical and/or
heuristic approaches to these problems in the same format.  As time
permits, we may also attempt to create an updated "Where do we go from
here?"  list, like that drawn up in last year's workshop. 

Video demos will be encouraged. If feasible, we will attempt to have a
VCR set up after the workshop to allow for informal demos.

Preparatory readings from the presenters will be ready by early
November. To be placed on a list to receive continuing information about
the workshop (such as where and when the readings appear on-line), send
email to "robot-learning-request at psyche.mit.edu".

Tentative Program: 
==================
December 3, 1993

Morning Session: Theoretical Approaches
---------------------------------------
7:30-8:30	Andrew Moore, CMU
		"The Parti-game approach to exploration"
synopses of
different	Leemon Baird, USAF
approaches	"Reinforcement learning in continuous domains"
(20 min each)
		Juergen Schmidhuber, TUM
		Reinforcement-directed information acquisition in
		Markov Environments

8:30-9:30	Open discussion

Afternoon Session: Heuristic Approaches
---------------------------------------
4:30-5:50       Long-Ji Lin, Siemens
                "RatBot: A mail-delivery robot"
synopses of
different       Stephan Schaal, MIT
approaches      "Efficiently exploring high-dimensional spaces"
(20 min each)
		Terry Sanger, MIT/JPL
		"Trajectory extension learning"

                Jeff Schneider, Rochester
		"Learning robot skills in high-dimensional action spaces"

5:50-6:30       Open discussion