Coupled learning of action parameters and forward models for manipulation

Abstract

The effectiveness of robot interaction depends on the robot's ability to perform task-relevant actions and on the degree to which it is able to predict the outcomes of these actions. In this paper we argue that the two learning problems - learning actions and learning forward models - must be tightly coupled for each of them to be successful. We present an approach that is able to learn a set of continuous action parameters and relational forward models from the robot's own experience. We formalize our approach as simultaneously clustering experiences in a continuous and a relational representation. Our experiments in a simulated manipulation experiment show that this form of coupled subsymbolic and symbolic learning is required for the robot to acquire task-relevant action capabilities.