5 papers
M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
Zuxing Lu, Ziang Zheng, Yao Lyu +7
Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomot…
UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
Yufei Jia, Zhanxiang Cao, Mingrui Yu +48
Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-ce…
Task-Centric Policy Optimization from Misaligned Motion Priors
Ziang Zheng, Kai Feng, Yi Nie +1
Humanoid control often leverages motion priors from human demonstrations to encourage natural behaviors. However, such demonstrations are frequently suboptimal or misaligned with r…
Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
Ziang Zheng, Guojian Zhan, Shiqi Liu +3
Reinforcement learning (RL) has shown great potential in enabling quadruped robots to perform agile locomotion. However, directly training policies to simultaneously handle dual ex…
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots
Ziang Zheng, Guojian Zhan, Bin Shuai +4
Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. T…