BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Huayi Wang, Zirui Ren, Junli Ben, Qingwei Huang, Tao Zhang, Weinan Pang, Jiangmiao |
| author_facet | Wang, Huayi Wang, Zirui Ren, Junli Ben, Qingwei Huang, Tao Zhang, Weinan Pang, Jiangmiao |
| contents | Traversing risky terrains with sparse footholds poses a significant challenge for humanoid robots, requiring precise foot placements and stable locomotion. Existing learning-based approaches often struggle on such complex terrains due to sparse foothold rewards and inefficient learning processes. To address these challenges, we introduce BeamDojo, a reinforcement learning (RL) framework designed for enabling agile humanoid locomotion on sparse footholds. BeamDojo begins by introducing a sampling-based foothold reward tailored for polygonal feet, along with a double critic to balancing the learning process between dense locomotion rewards and sparse foothold rewards. To encourage sufficient trial-and-error exploration, BeamDojo incorporates a two-stage RL approach: the first stage relaxes the terrain dynamics by training the humanoid on flat terrain while providing it with task-terrain perceptive observations, and the second stage fine-tunes the policy on the actual task terrain. Moreover, we implement a onboard LiDAR-based elevation map to enable real-world deployment. Extensive simulation and real-world experiments demonstrate that BeamDojo achieves efficient learning in simulation and enables agile locomotion with precise foot placement on sparse footholds in the real world, maintaining a high success rate even under significant external disturbances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds Wang, Huayi Wang, Zirui Ren, Junli Ben, Qingwei Huang, Tao Zhang, Weinan Pang, Jiangmiao Robotics Artificial Intelligence Machine Learning Traversing risky terrains with sparse footholds poses a significant challenge for humanoid robots, requiring precise foot placements and stable locomotion. Existing learning-based approaches often struggle on such complex terrains due to sparse foothold rewards and inefficient learning processes. To address these challenges, we introduce BeamDojo, a reinforcement learning (RL) framework designed for enabling agile humanoid locomotion on sparse footholds. BeamDojo begins by introducing a sampling-based foothold reward tailored for polygonal feet, along with a double critic to balancing the learning process between dense locomotion rewards and sparse foothold rewards. To encourage sufficient trial-and-error exploration, BeamDojo incorporates a two-stage RL approach: the first stage relaxes the terrain dynamics by training the humanoid on flat terrain while providing it with task-terrain perceptive observations, and the second stage fine-tunes the policy on the actual task terrain. Moreover, we implement a onboard LiDAR-based elevation map to enable real-world deployment. Extensive simulation and real-world experiments demonstrate that BeamDojo achieves efficient learning in simulation and enables agile locomotion with precise foot placement on sparse footholds in the real world, maintaining a high success rate even under significant external disturbances. |
| title | BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.10363 |