An Adaptive Imitation Learning Framework for Robotic Complex Contact-Rich Insertion Tasks.

ABSTRACT: Complex contact-rich insertion is a ubiquitous robotic manipulation skill and usually involves nonlinear and low-clearance insertion trajectories as well as varying force requirements. A hybrid trajectory and force learning framework can be utilized to generate high-quality trajectories by imitation learning and find suitable force control policies efficiently by reinforcement learning. However, with the mentioned approach, many human demonstrations are necessary to learn several tasks even when those tasks require topologically similar trajectories. Therefore, to reduce human repetitive teaching efforts for new tasks, we present an adaptive imitation framework for robot manipulation. The main contribution of this work is the development of a framework that introduces dynamic movement prim

SUBMITTER: Wang Y 

PROVIDER: S-EPMC8787218 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature