Neuroadaptation in Physical Human-Robot Collaboration
- URL: http://arxiv.org/abs/2310.00351v1
- Date: Sat, 30 Sep 2023 12:16:24 GMT
- Title: Neuroadaptation in Physical Human-Robot Collaboration
- Authors: Avinash Singh, Dikai Liu, Chin-Teng Lin
- Abstract summary: 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.
The experiment results show that the closed-loop-based neuroadaptive framework successfully reduces the level of cognitive conflict.
- Score: 34.73541717674098
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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