Multi-sensory Integration in a Quantum-Like Robot Perception Model
This work addresses robot perception for systems with limited sensing, but it appears incremental as it builds on a preliminary study.
The authors tackled the problem of robot perception by generalizing a quantum-like model to handle multi-sensory inputs, resulting in a compact representation that enables query operators to quantify belief in world states.
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.