A correlational analysis of multiagent sensorimotor interactions: clustering autonomous and controllable entities

TL;DR

This paper explores how sensorimotor interaction data can be used to distinguish between different agent types and intentions, advancing the understanding of how synthetic agents can develop Theory of Mind abilities.

Contribution

It introduces a dual-arm robotic setup and correlation analysis methods to identify agent typology and intentions from sensorimotor data, highlighting missing elements for ToM development.

Findings

Correlation measures can distinguish self from other.

Sensorimotor data can differentiate active from inanimate objects.

Discussion of necessary architectural elements for ToM acquisition.

Abstract

A first step to reach Theory of Mind (ToM) abilities (attribution of beliefs to others) in synthetic agents through sensorimotor interactions, would be to tag sensory data with agent typology and action intentions: autonomous agent X moved an object under the box. We propose a dual arm robotic setup in which ToM could be probed. We then discuss what measures can be extracted from sensorimotor interaction data (based on a correlation analysis) in the proposed setup that allow to distinguish self than other and other/inanimate from other/active with intentions. We finally discuss what elements are missing in current cognitive architectures to be able to acquire ToM abilities in synthetic agents from sensorimotor interactions, bottom-up from reactive agent interaction behaviors and top-down from the optimization of social behaviour and cooperation.