Sensorimotor Abstraction Selection for Efficient, Autonomous Robot Skill Acquisition
Publication Date
2008
Journal or Book Title
2008 IEEE 7TH INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING
Abstract
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-specific 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 fitting 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.
DOI
https://doi.org/10.1109/DEVLRN.2008.4640821
Pages
151-156
Book Series Title
IEEE International Conference on Development and Learning
Recommended Citation
Konidaris, G and Barto, A, "Sensorimotor Abstraction Selection for Efficient, Autonomous Robot Skill Acquisition" (2008). 2008 IEEE 7TH INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING. 328.
https://doi.org/10.1109/DEVLRN.2008.4640821