Abstract
Insertion is an essential skill for robots in both modern manufacturing and services robotics. In our previous study, we proposed an insertion skill framework based on forcedomain wiggle motion. The main limitation of this method lies in the robot’s inability to adjust its behavior according to changing contact state during interaction. In this paper, we extend the skill formalism by incorporating a behavior tree-based primitive switching mechanism that leverages highfrequency tactile data for the estimation of contact state. The efficacy of our proposed framework is validated with a series of experiments that involve the execution of tightly constrained peg-in-hole tasks. The experiment results demonstrate a significant improvement in performance, characterized by reduced execution time, heightened robustness, and superior adaptability when confronted with unknown tasks. Moreover, in the context of transfer learning, our paper provides empirical evidence indicating that the proposed skill framework contributes to enhanced transferability across distinct operational contexts and tasks.