Iterative assist-as-needed control with skill learning for physical human-robot interaction

Abstract

Collaborative robots referring to physical human-robot interaction applications have drawn considerable attention. There have been lots of methods based on skill learning to help robots mimic human collaborative behaviors. However, the stiffness profiles generated by these methods only rely on the quality of collected human demonstrations and cannot be adjusted according to detailed interaction tasks. To this end, this paper combines a skill learning method and an iterative assist-as-needed impedance controller. The skill learning method encodes and generates position and stiffness references. The iterative assist-as-needed controller enables the robot to adjust the generated stiffness profile actively according to the human intention. When the human tries to act as an active role in interaction tasks, the controller takes a lower stiffness parameter, which means it can tolerate more deviation from the desired trajectory, and vice versa. Compared to the previous results, on the one hand, this paper employs a skill learning method to generate an initial stiffness profile for the iterative assist-as-needed controller, which enables the robot to mimic human behaviors; on the other hand, the controller proposed in this paper allows the manual design of the damping and mass parameters while the previous controller accepts only the desired stiffness parameter. Finally, one simulation and one experiment are demonstrated to evaluate the performance of the designed controller.