From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911132664463360 |
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| author | Wang, Yuxuan Yang, Ming Ding, Ziluo Zhang, Yu Zeng, Weishuai Xu, Xinrun Jiang, Haobin Lu, Zongqing |
| author_facet | Wang, Yuxuan Yang, Ming Ding, Ziluo Zhang, Yu Zeng, Weishuai Xu, Xinrun Jiang, Haobin Lu, Zongqing |
| contents | Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they struggle to generalize across highly varied behaviors due to conflicting control requirements and mismatched data distributions. In this work, we propose BumbleBee (BB), an expert-generalist learning framework that combines motion clustering and sim-to-real adaptation to overcome these challenges. BB first leverages an autoencoder-based clustering method to group behaviorally similar motions using motion features and motion descriptions. Expert policies are then trained within each cluster and refined with real-world data through iterative delta action modeling to bridge the sim-to-real gap. Finally, these experts are distilled into a unified generalist controller that preserves agility and robustness across all motion types. Experiments on two simulations and a real humanoid robot demonstrate that BB achieves state-of-the-art general whole-body control, setting a new benchmark for agile, robust, and generalizable humanoid performance in the real world. The project webpage is available at https://beingbeyond.github.io/BumbleBee/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12779 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots Wang, Yuxuan Yang, Ming Ding, Ziluo Zhang, Yu Zeng, Weishuai Xu, Xinrun Jiang, Haobin Lu, Zongqing Robotics Machine Learning Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they struggle to generalize across highly varied behaviors due to conflicting control requirements and mismatched data distributions. In this work, we propose BumbleBee (BB), an expert-generalist learning framework that combines motion clustering and sim-to-real adaptation to overcome these challenges. BB first leverages an autoencoder-based clustering method to group behaviorally similar motions using motion features and motion descriptions. Expert policies are then trained within each cluster and refined with real-world data through iterative delta action modeling to bridge the sim-to-real gap. Finally, these experts are distilled into a unified generalist controller that preserves agility and robustness across all motion types. Experiments on two simulations and a real humanoid robot demonstrate that BB achieves state-of-the-art general whole-body control, setting a new benchmark for agile, robust, and generalizable humanoid performance in the real world. The project webpage is available at https://beingbeyond.github.io/BumbleBee/. |
| title | From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2506.12779 |