On the Sensory Commutativity of Action Sequences for Embodied Agents

Hugo Caselles-Dupré (U2IS, ENSTA Paris, Institut Polytechnique de Paris & INRIA), Michael Garcia-Ortiz (CitAI, SMCSE, City University of London), David Filliat (U2IS, ENSTA Paris, Institut Polytechnique de Paris & INRIA)

Abstract

We study perception in the scenario of an embodied agent equipped with first-person sensors and a continuous motor space with multiple degrees of freedom. We consider the commutative properties of action sequences with respect to sensory information perceived by such an embodied agent. We introduce the Sensory Commutativity Probability (SCP) criterion which measures how much an agent's degree of freedom affects the environment in embodied scenarios. We show how to compute this criterion in different environments, including realistic robotic setups and discuss how SCP and the commutative properties of action sequences can be used to learn about objects in the environment and improve sample-efficiency in Reinforcement Learning.