UniInsertion: A Unified Model-based Insertion Skill Learning via Differentiable Physics-based Simulation

Chenrui Tie, Wang Debang, Gaurav Chaudhary, Weikun Peng, Tianyi Chen, Gang Yang, Yao Mu, Lin Shao

Keywords: Differentiable Simulation; Robot Manipulation

Abstract:

Manipulating and inserting deformable objects into tight spaces remains challenging in robotics due to their flexibility, intricate configurations, and complex contact dynamics. Prior methods relying on analytical models or human demonstrations often struggle to generalize across diverse scenarios. This paper presents a model-based framework leveraging differentiable physics simulation and the innovative concept of "learning from reversal" to enable robotic insertion of both rigid and deformable objects.
Our key insight is that while insertion presents difficulties, the reverse process of insertion can provide clearer intermediate waypoints, and we posit that frames with a higher number of collision points during the insertion process are more likely to represent critical waypoints. By discerning these waypoints through learning from reversals, we obtain a smooth, differentiable transition from waypoint identification to trajectory optimization via the differentiable simulator.
Furthermore, we construct an extensive dataset with the simulator, encompassing diverse object shapes, materials, and container geometries, with corresponding demonstrations. This powers imitation learning to train robust policies, showcasing adaptability to novel objects and containers.
Our framework, integrating learning from reversals, differentiable physics, and imitation learning, pioneers a paradigm shift in robotic insertion capabilities. Evaluations demonstrate superiority over competing approaches in sample efficiency, performance, and sim-to-real transfer. Supplementary and Videos are on the website: https://sites.google.com/view/uniinsertion

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 4721