SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

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;

Read original source ↗ Open in workspace

recordType

paper

region

Global