A Preliminary Study for a Quantum-like Robot Perception Model

TL;DR

This paper explores the potential of quantum-like models to enhance robot perception by simulating quantum behaviors, highlighting their advantages in knowledge representation despite current implementation challenges.

Contribution

It introduces a quantum-like perception model for robots, analyzes its feasibility through simulations, and discusses limitations and benefits over traditional approaches.

Findings

Quantum-like models can simulate complex robot perception behaviors.

Simulations show potential advantages in knowledge processing.

Quantum device errors limit real-world implementations.

Abstract

Formalisms based on quantum theory have been used in Cognitive Science for decades due to their descriptive features. A quantum-like (QL) approach provides descriptive features such as state superposition and probabilistic interference behavior. Moreover, quantum systems dynamics have been found isomorphic to cognitive or biological systems dynamics. The objective of this paper is to study the feasibility of a QL perception model for a robot with limited sensing capabilities. We introduce a case study, we highlight its limitations, and we investigate and analyze actual robot behaviors through simulations, while actual implementations based on quantum devices encounter errors for unbalanced situations. In order to investigate QL models for robot behavior, and to study the advantages leveraged by QL approaches for robot knowledge representation and processing, we argue that it is preferable to proceed with simulation-oriented techniques rather than actual realizations on quantum backends.