{"ID":2870635,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13534","arxiv_id":"2509.13534","title":"Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning","abstract":"Whole-body manipulation (WBM) for humanoid robots presents a promising approach for executing embracing tasks involving bulky objects, where traditional grasping relying on end-effectors only remains limited in such scenarios due to inherent stability and payload constraints. This paper introduces a reinforcement learning framework that integrates a pre-trained human motion prior with a neural signed distance field (NSDF) representation to achieve robust whole-body embracing. Our method leverages a teacher-student architecture to distill large-scale human motion data, generating kinematically natural and physically feasible whole-body motion patterns. This facilitates coordinated control across the arms and torso, enabling stable multi-contact interactions that enhance the robustness in manipulation and also the load capacity. The embedded NSDF further provides accurate and continuous geometric perception, improving contact awareness throughout long-horizon tasks. We thoroughly evaluate the approach through comprehensive simulations and real-world experiments. The results demonstrate improved adaptability to diverse shapes and sizes of objects and also successful sim-to-real transfer. These indicate that the proposed framework offers an effective and practical solution for multi-contact and long-horizon WBM tasks of humanoid robots.","short_abstract":"Whole-body manipulation (WBM) for humanoid robots presents a promising approach for executing embracing tasks involving bulky objects, where traditional grasping relying on end-effectors only remains limited in such scenarios due to inherent stability and payload constraints. This paper introduces a reinforcement learn...","url_abs":"https://arxiv.org/abs/2509.13534","url_pdf":"https://arxiv.org/pdf/2509.13534v1","authors":"[\"Chunxin Zheng\",\"Kai Chen\",\"Zhihai Bi\",\"Yulin Li\",\"Liang Pan\",\"Jinni Zhou\",\"Haoang Li\",\"Jun Ma\"]","published":"2025-09-16T21:01:24Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}