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.

通过应用认知冲突信息，利用强化学习在闭环环境中调整机器人策略，并与开环设置进行对比，实验结果表明，闭环神经自适应框架成功地降低了物理人机协作中的认知冲突水平，从而提高了人机协作的平滑性和直观性。这些结果显示出了通过脑电图 (EEG) 信号的神经自适应方法在未来物理人机协作控制系统中的可行性。

物理人机协作中的神经适应