Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911652702584832 |
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| author | Zhao, Yingnan Wang, Xinmiao Wang, Dewei Liu, Xinzhe Lu, Dan Han, Qilong Liu, Peng Bai, Chenjia |
| author_facet | Zhao, Yingnan Wang, Xinmiao Wang, Dewei Liu, Xinzhe Lu, Dan Han, Qilong Liu, Peng Bai, Chenjia |
| contents | Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregular terrains and in diverse situations. To address this challenge, we propose Adaptive Humanoid Control (AHC) that adopts a two-stage framework to learn an adaptive humanoid locomotion controller across different skills and terrains. Specifically, we first train several primary locomotion policies and perform a multi-behavior distillation process to obtain a basic multi-behavior controller, facilitating adaptive behavior switching based on the environment. Then, we perform reinforced fine-tuning by collecting online feedback in performing adaptive behaviors on more diverse terrains, enhancing terrain adaptability for the controller. We conduct experiments in both simulation and real-world experiments in Unitree G1 robots. The results show that our method exhibits strong adaptability across various situations and terrains. Project website: https://ahc-humanoid.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning Zhao, Yingnan Wang, Xinmiao Wang, Dewei Liu, Xinzhe Lu, Dan Han, Qilong Liu, Peng Bai, Chenjia Robotics Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregular terrains and in diverse situations. To address this challenge, we propose Adaptive Humanoid Control (AHC) that adopts a two-stage framework to learn an adaptive humanoid locomotion controller across different skills and terrains. Specifically, we first train several primary locomotion policies and perform a multi-behavior distillation process to obtain a basic multi-behavior controller, facilitating adaptive behavior switching based on the environment. Then, we perform reinforced fine-tuning by collecting online feedback in performing adaptive behaviors on more diverse terrains, enhancing terrain adaptability for the controller. We conduct experiments in both simulation and real-world experiments in Unitree G1 robots. The results show that our method exhibits strong adaptability across various situations and terrains. Project website: https://ahc-humanoid.github.io. |
| title | Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.06371 |