Humanoid motion tracking provides a scalable interface for transferring kinematic human motion to whole-body robot control, but highly dynamic and contact-rich references remain difficult because underactuation and discontinuous contacts can restrict the closed-loop state distributions explored during training. Exter...
Chao-Jie Fu, Cheng-Kai Su, Lei Jiang et al.· Frontiers in Neurorobotics· 0 citations
Achieving biological-level running speeds has largely been pursued through advances in control algorithms, which improve the utilization of existing hardware. However, the ultimate speed limits remain governed by the underlying force and torque requirements of rapid locomotion, which are typically addressed through inc...
Yu-Cheng Tao, Yong-Bin Jin, Shao-wen Cheng et al.· 0 citations
A refined actuator model explicitly captures high-speed voltage coupling and magnetic saturation, enabling a more accurate representation of the torque–speed envelope and a reinforcement learning framework incorporating a two-stage curriculum and adaptive command scheduling (ACS) ensures stable training.
Yu-Cheng Tao, Shao-Wen Cheng, Guo-Rong Lan et al.· IEEE Robotics and Automation...· 0 citations
Perceptive General Motion Tracking pipeline for humanoid robots that learns terrain adaptation from independently selected motion references and terrains, providing a unified policy for terrain-adaptive locomotion, diverse whole-body behaviors, and teleoperation in complex environments.
Hong-Yi Li, Pei-Zhuo Li, Yu-Cheng Tao et al.· 2 citations
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