4 papers
Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies
Penghui Chen, Tinglong Zheng, Yufeng Zhang +1
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,…
Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots
Yushi Wang, Changsheng Luo, Penghui Chen +7
Humanoid soccer poses a representative challenge for embodied intelligence, requiring robots to coordinate agile locomotion with unreliable visual perception in dynamic environment…
Booster Gym: An End-to-End Reinforcement Learning Framework for Humanoid Robot Locomotion
Yushi Wang, Penghui Chen, Xinyu Han +2
Recent advancements in reinforcement learning (RL) have led to significant progress in humanoid robot locomotion, simplifying the design and training of motion policies in simulati…
HiFAR: Multi-Stage Curriculum Learning for High-Dynamics Humanoid Fall Recovery
Penghui Chen, Yushi Wang, Changsheng Luo +2
Humanoid robots encounter considerable difficulties in autonomously recovering from falls, especially within dynamic and unstructured environments. Conventional control methodologi…