Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916953082298368 |
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| author | Zheng, Chunxin Chen, Kai Bi, Zhihai Li, Yulin Pan, Liang Zhou, Jinni Li, Haoang Ma, Jun |
| author_facet | Zheng, Chunxin Chen, Kai Bi, Zhihai Li, Yulin Pan, Liang Zhou, Jinni Li, Haoang Ma, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning Zheng, Chunxin Chen, Kai Bi, Zhihai Li, Yulin Pan, Liang Zhou, Jinni Li, Haoang Ma, Jun Robotics 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. |
| title | Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.13534 |