Sensorimotor abstraction selection for efficient, autonomous robot skill acquisition

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.