Neuroadaptation in Physical Human-Robot Collaboration

TL;DR

This paper introduces a neuroadaptive framework for physical human-robot collaboration that uses EEG signals and reinforcement learning to reduce cognitive conflict and improve collaboration smoothness.

Contribution

It presents a novel closed-loop neuroadaptive approach that dynamically tunes robot behavior based on cognitive conflict signals, enhancing pHRC performance.

Findings

Reduces cognitive conflict during collaboration

Increases smoothness and intuitiveness of human-robot interaction

Demonstrates feasibility of EEG-based neuroadaptation

Abstract

Robots for physical Human-Robot Collaboration (pHRC) systems need to change their behavior and how they operate in consideration of several factors, such as the performance and intention of a human co-worker and the capabilities of different human-co-workers in collision avoidance and singularity of the robot operation. As the system's admittance becomes variable throughout the workspace, a potential solution is to tune the interaction forces and control the parameters based on the operator's requirements. To overcome this issue, we have demonstrated a novel closed-loop-neuroadaptive framework for pHRC. We have applied cognitive conflict information in a closed-loop manner, with the help of reinforcement learning, to adapt to robot strategy and compare this with open-loop settings. The experiment results show that the closed-loop-based neuroadaptive framework successfully reduces the level of cognitive conflict during pHRC, consequently increasing the smoothness and intuitiveness of human-robot collaboration. These results suggest the feasibility of a neuroadaptive approach for future pHRC control systems through electroencephalogram (EEG) signals.