A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition

TL;DR

This paper presents a neurorobotics approach for behavior selection in robots based on human activity recognition, emphasizing the handling of uncertainty and neurophysiological modeling, outperforming heuristic methods in simulation.

Contribution

Introduces a bioinspired neurorobotics method for behavior selection that accounts for uncertainty in activity recognition, compared to traditional deterministic approaches.

Findings

Neurorobotics approach outperforms heuristics in correct task execution.

Complex animal-based models yield better results.

Simulation demonstrates effectiveness in smart home scenarios.

Abstract

Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact effectively and autonomously with humans, the coupling between techniques for human activity recognition, based on sensing information, and robot behaviour selection, based on decision-making mechanisms, is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting a neurorobotics approach based on computational models that resemble neurophysiological aspects of living beings. This neurorobotics approach was compared to a non-bioinspired, heuristics-based approach. To evaluate both approaches, a robot simulation is developed, in which a mobile robot has to accomplish tasks according to the activity being performed by the inhabitant of an intelligent home. The outcomes of each approach were evaluated according to the number of correct outcomes provided by the robot. Results revealed that the neurorobotics approach is advantageous, especially considering the computational models based on more complex animals.