Neuroadaptation in Physical Human-Robot Collaboration
This addresses the challenge of improving human-robot interaction in pHRC systems, though it appears incremental as it builds on existing neuroadaptive and reinforcement learning methods.
The paper tackles the problem of adapting robot behavior in physical human-robot collaboration (pHRC) by proposing a closed-loop neuroadaptive framework that uses cognitive conflict information and reinforcement learning, resulting in reduced cognitive conflict and increased smoothness and intuitiveness of collaboration.
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.