Synthesis, learning and abstraction of skills through parameterized smooth map from sensors to behaviors

Abstract

The integration theory of reactive behaviors is to be discussed in this paper. A linear emerging model is adopted where the motion of a robot is represented as the weighted linear sum of reactive behaviors. The weights are defined as differentiable nonlinear functions of sensor signals and parameters. We proposed approaches toward skill learning and skill abstraction based on the sensor space model, where the parameters are systematically tuned through iteration of trials such that the sensor signals converge to the given teacher signals. The learning algorithm and the abstraction algorithm are experimentally applied to the reactive grasp of a three-fingered robot hand. The experimental results illustrate the effectiveness of the proposed algorithms.