A Modular Dynamic Sensorimotor Model for Affordances Learning, Sequences Planning, and Tool-Use

This paper proposes a computational model for learning robot control and sequence planning based on the ideomotor principle. This model encodes covariation laws between sensors and motors in a modular fashion and exploits these primitive skills to build complex action sequences, potentially involving tool-use. Implemented for a robotic arm, the model starts with raw unlabeled sensor and motor vectors and autonomously assigns functions to neutral objects in the environment. Our experimental evaluation highlights the emergent properties of such a modular system and we discuss their consequences from ideomotor and sensorimotor-theoretic perspectives.