Towards Human-level Intelligence via Human-like Whole-Body Manipulation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909701759827968 |
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| author | Gao, Guang Wang, Jianan Zuo, Jinbo Jiang, Junnan Zhang, Jingfan Zeng, Xianwen Zhu, Yuejiang Ma, Lianyang Chen, Ke Sheng, Minhua Zhang, Ruirui An, Zhaohui |
| author_facet | Gao, Guang Wang, Jianan Zuo, Jinbo Jiang, Junnan Zhang, Jingfan Zeng, Xianwen Zhu, Yuejiang Ma, Lianyang Chen, Ke Sheng, Minhua Zhang, Ruirui An, Zhaohui |
| contents | Building general-purpose intelligent robots has long been a fundamental goal of robotics. A promising approach is to mirror the evolutionary trajectory of humans: learning through continuous interaction with the environment, with early progress driven by the imitation of human behaviors. Achieving this goal presents three core challenges: (1) designing safe robotic hardware with human-level physical capabilities; (2) developing an intuitive and scalable whole-body teleoperation interface for data collection; and (3) creating algorithms capable of learning whole-body visuomotor policies from human demonstrations. To address these challenges in a unified framework, we propose Astribot Suite, a robot learning suite for whole-body manipulation aimed at general daily tasks across diverse environments. We demonstrate the effectiveness of our system on a wide range of activities that require whole-body coordination, extensive reachability, human-level dexterity, and agility. Our results show that Astribot's cohesive integration of embodiment, teleoperation interface, and learning pipeline marks a significant step towards real-world, general-purpose whole-body robotic manipulation, laying the groundwork for the next generation of intelligent robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Human-level Intelligence via Human-like Whole-Body Manipulation Gao, Guang Wang, Jianan Zuo, Jinbo Jiang, Junnan Zhang, Jingfan Zeng, Xianwen Zhu, Yuejiang Ma, Lianyang Chen, Ke Sheng, Minhua Zhang, Ruirui An, Zhaohui Robotics Artificial Intelligence Building general-purpose intelligent robots has long been a fundamental goal of robotics. A promising approach is to mirror the evolutionary trajectory of humans: learning through continuous interaction with the environment, with early progress driven by the imitation of human behaviors. Achieving this goal presents three core challenges: (1) designing safe robotic hardware with human-level physical capabilities; (2) developing an intuitive and scalable whole-body teleoperation interface for data collection; and (3) creating algorithms capable of learning whole-body visuomotor policies from human demonstrations. To address these challenges in a unified framework, we propose Astribot Suite, a robot learning suite for whole-body manipulation aimed at general daily tasks across diverse environments. We demonstrate the effectiveness of our system on a wide range of activities that require whole-body coordination, extensive reachability, human-level dexterity, and agility. Our results show that Astribot's cohesive integration of embodiment, teleoperation interface, and learning pipeline marks a significant step towards real-world, general-purpose whole-body robotic manipulation, laying the groundwork for the next generation of intelligent robots. |
| title | Towards Human-level Intelligence via Human-like Whole-Body Manipulation |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2507.17141 |