Compliant Robust Control for Robotic Insertion of Soft Bodies

Abstract

This paper proposes a novel framework for insertion-type tasks with soft bodies, such as cleaning a bottle with a soft brush. First, a multimodal model based on vision and force perception is trained. Domain randomization is used for the soft body's properties to overcome the simulation-to- reality gap. Second, we propose a dynamic safety lock method based on force perception, which is embedded in the training model to make sure that the tool explores and traverses the hole's path in a compliant way. This result in a higher success rate without damaging the tools/holes. Finally, we perform experiments in simulation and the real world, and the success rate of our proposed method reaches 85.14% in simulation and 83.45% in the real world. Ablation experiments in the real world demonstrate that our method is effective for complex paths and soft bodies with varying deformation intensities. Videos and code are supplied in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://0707yiliu.github.io/SoftBodyInsertion/.</uri>