  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.
