Skill Learning Strategy Based on Dynamic Motion Primitives for Human-Robot Cooperative Manipulation

This article presents a skill learning-based hierarchical control strategy for human-robot cooperative manipulation, which constitutes a novel learning-control system. The high-level learning strategy aims to learn the motor skills from human demonstrations by fusion with dynamic motion primitives (DMPs) and the Gaussian mixture model (GMM). The lower level control strategy guarantees the compliance of the robot movement under human interaction using admittance control and integral barrier Lyapunov function (IBLF)-based adaptive neural controller. First, the robot learns the motor skills from observing the successful execution of tasks by a demonstrator through DMP-GMM methods. Then, the robot reproduces the complex skills and executes the interactive task by demonstrations. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with experimental results. The results show that the developed hierarchical strategy has good performance in cooperation by learning and control that reacts compliantly to robot interaction with human subjects.

Automated summary

This article presents a hierarchical control strategy for human-robot cooperative manipulation based on skill learning, utilizing high-level learning and low-level control strategies to achieve compliant robot movement and interactive task execution.