Multi-sensory Integration in a Quantum-Like Robot Perception Model

TL;DR

This paper introduces a quantum-inspired multi-sensory perception model for robots that effectively represents uncertainty and decision-making, using a multidimensional framework based on sensor data.

Contribution

It generalizes previous quantum-like perception models to handle multiple sensory inputs, providing a compact representation for uncertainty and decision processes.

Findings

Model offers a compact, elegant representation of sensory data.

Enables natural definition of query operators for belief assessment.

Demonstrated on a 3D case study.

Abstract

Formalisms inspired by Quantum theory have been used in Cognitive Science for decades. Indeed, Quantum-Like (QL) approaches provide descriptive features that are inherently suitable for perception, cognition, and decision processing. A preliminary study on the feasibility of a QL robot perception model has been carried out for a robot with limited sensing capabilities. In this paper, we generalize such a model for multi-sensory inputs, creating a multidimensional world representation directly based on sensor readings. Given a 3-dimensional case study, we highlight how this model provides a compact and elegant representation, embodying features that are extremely useful for modeling uncertainty and decision. Moreover, the model enables to naturally define query operators to inspect any world state, which answers quantifies the robot's degree of belief on that state.