Contact-Guided Exploration for Non-Prehensile Locomanipulation with Multi-Critic RL

Abstract:Non-prehensile manipulation offers versatile skills for moving and rearranging heavy or bulky objects, particularly when combined with a mobile manipulation platform. However, both model-based and model-free approaches struggle with the complex hybrid dynamics and the sparsity of the contact in these tasks. To address these challenges, we propose a contact-guided exploration strategy implemented within a Multi-Critic Reinforcement Learning (RL) framework. A dedicated exploration critic is trained with a dense contact-seeking reward that guides the end-effector toward meaningful contact points; its influence is progressively decayed to recover a task-optimal policy. We obtain candidate interaction points from a general-purpose grasping algorithm, enabling the exploration mechanism to generalise across various object geometries. We evaluate the approach on multiple tasks, including box pushing, chair transportation, and a dishwasher opening task. Finally, we validate the chair transportation policy through extensive experiments on a quadrupedal mobile manipulator, demonstrating deployable non-prehensile manipulation in the real world.

Robotics (cs.RO)

arXiv:2608.28140 [cs.RO]

(or arXiv:2608.28140v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.28140 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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