- 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.
- Copyright:
- 2008 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- No links available
- Bibtex format
- @InProceedings{EDI-INF-RR-1309,
- author = {
Matthew Howard
and Stefan Klanke
and Gienger Michael
and Goerick Christian
and Sethu Vijayakumar
},
- title = {Learning Potential-based Policies from Constrained Motion},
- book title = {IEEE International Conference on Humanoid Robots},
- publisher = {IEEE Press},
- year = 2008,
- month = {Dec},
- }
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