Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies

• URL: http://arxiv.org/abs/2606.27813v1
• Date: Fri, 26 Jun 2026 07:55:15 GMT
• Title: Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies
• Abstract summary: Humanoid robot motion learning requires task-oriented control policies and behaviors that can be transferred to real robots.<n>We propose a data-centric training and deployment pipeline that integrates motion data curation, real-to-sim model adaptation, AMP-based reinforcement learning, and sim-to-real deployment.
• Score: 5.514697741511785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humanoid robot motion learning requires not only task-oriented control policies but also physically feasible and natural behaviors that can be transferred to real robots. However, robot-feasible motion data are often scarce: raw human demonstrations may be incompatible with the robot morphology, open-source clips vary in quality, and simulation-collected robot trajectories still require feasibility checking. To address these challenges, we propose a data-centric training and deployment pipeline that integrates motion data curation, real-to-sim model adaptation, AMP-based reinforcement learning, and sim-to-real deployment. We validate the framework on the Booster T1 robot and further provide preliminary cross-platform validation on Booster K1.

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