An Perception Enhanced Human-robot Skill Transfer Method for Reactive Interaction

In this paper, we propose a trust human-robot skill transfer framework, interpretive and reactive dynamic system (IRDS), by investigating the behaviour tree (BT) and dynamic movement primitives (DMPs) enhanced by the feedback from perceived information for dynamic and uncertain tasks and environments. Human sensorimotor control allows interactions with various environments and accomplishes complex manipulation tasks with uncertainty; however, it is still hard for robots to own this capability. In this work, we aim to transfer these reactive skills to robots, which enable robots to interact with humans and environments under varying uncertainty. The main challenges of robot skill learning are the generalization, safety and stability issues during the skills learning and execution autonomously. BT was investigated for task planning and reactive interaction during the robot execution. The dynamic system-based model generates action for the low-level compliant controller. The convergence of the proposed IRDS framework under dynamic interaction and disturbance was proved by control theory. We conducted simulations and physical experiments on the real robots to evaluate the generalization performance and the convergence capability under uncertainty. And the results show that the reactivity, convergence and interaction performance can be guaranteed, and the learned skills can be transferred among different physical robot platforms.