Hybrid Contact Dynamics and Residual-RL Framework for Multi-Point Object Pushing

Abstract

Robotic contact manipulation involves applying controlled forces at contact points to guide an object along a desired trajectory while respecting the underlying physical interactions. This paper presents a novel framework that integrates dynamic modeling and Reinforcement Learning (RL) to achieve robust object pushing with a redundant robotic arm. First, a comprehensive dynamic contact model is formulated, incorporating unilateral constraints and a box friction model to capture the nonlinearities present in real-world contact dynamics. Second, the model is extended to handle multiple simultaneous point contacts, enabling effective trajectory planning and tracking for a redundant robotic arm in multi-contact pushing tasks. Third, an RL strategy is introduced as a residual module that augments a model-based controller to improve pushing performance. Simulation and real-world experiments with a Kinova Gen2 arm demonstrate that the proposed method achieves accurate trajectory following and stable contact interactions, significantly outperforming traditional PD control strategies in dynamic pushing scenarios.