Learning Potential-based Policies from Constrained Motion
Proc. 8th IEEE-RAS International Conference on Humanoid Robots (Humanoids)
Date
2008Author
Howard, Matthew
Klanke, Stefan
Gienger, Michael
Goerick, Christian
Vijayakumar, Sethu
Metadata
Abstract
We present a method for learning potential-based
policies from constrained motion data. In contrast to previous
approaches to direct policy learning, our method can combine observations
from a variety of contexts where different constraints
are in force, to learn the underlying unconstrained policy in form
of its potential function. This allows us to generalise and predict
behaviour where novel constraints apply. As a key ingredient, we
first create multiple simple local models of the potential, and align
those using an efficient algorithm.We can then detect and discard
unsuitable subsets of the data and learn a global model from a
cleanly pre-processed training set. We demonstrate our approach
on systems of varying complexity, including kinematic data from
the ASIMO humanoid robot with 22 degrees of freedom.