Sensorimotor abstraction selection for efficient, autonomous robot skill acquisition

Journal Article

To achieve truly autonomous robot skill acquisition, a robot can use neither a single large general state space (because learning is not feasible), nor a small problem-speci c state space (because it is not general).We propose that instead a robot should have a set of sensorimotor abstractions that can be considered small candidate state spaces, and select one that is appropriate for learning a skill when it decides to do so. We introduce an incremental algorithm that selects a state space in which to learn a skill from among a set of potential spaces given a successful sample trajectory. The algorithm returns a policy tting that trajectory in the new state space so that learning does not have to begin from scratch. We demonstrate that the algorithm selects an appropriate space for a sequence of demonstration skills on a physically realistic simulated mobile robot, and that the resulting initial policies closely match the sample trajectory. ©2008 IEEE.

Full Text

Duke Authors

Cited Authors

  • Konidaris, G; Barto, A

Published Date

  • December 1, 2008

Published In

  • 2008 IEEE 7th International Conference on Development and Learning, ICDL

Start / End Page

  • 151 - 156

Digital Object Identifier (DOI)

  • 10.1109/DEVLRN.2008.4640821

Citation Source

  • Scopus