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

2026-08-28

AI Summary

为解决非抓握操作中的复杂动态和接触稀疏问题，提出了一种基于多评论家强化学习框架的接触引导探索策略。

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.

Problem

• non-prehensile manipulation
• hybrid dynamics
• contact sparsity

Innovation

• Contact-Guided Exploration
• Multi-Critic Reinforcement Learning
• Dense Contact-Seeking Reward