BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds

• URL: http://arxiv.org/abs/2502.10363v3
• Date: Sun, 27 Apr 2025 13:49:42 GMT
• Title: BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds
• Authors: Huayi Wang, Zirui Wang, Junli Ren, Qingwei Ben, Tao Huang, Weinan Zhang, Jiangmiao Pang,
• Abstract summary: Existing learning-based approaches often struggle on complex terrains due to sparse foothold rewards and inefficient learning processes.<n>We introduce BeamDojo, a reinforcement learning framework designed for enabling agile humanoid locomotion on sparse footholds.<n>We show that BeamDojo achieves efficient learning in simulation and enables agile locomotion with precise foot placement on sparse footholds in the real world.
• Score: 35.62230804783507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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