8 papers
Discovering Self-Protective Falling Policy for Humanoid Robot via Deep Reinforcement Learning
Diyuan Shi, Shangke Lyu, Donglin Wang
Humanoid robots have received significant research interests and advancements in recent years. Despite many successes, due to their morphology, dynamics and limitation of control p…
Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control
Renjie Wang, Shangke Lyu, Donglin Wang
While Reinforcement Learning (RL) has achieved remarkable progress in legged locomotion control, it often suffers from performance degradation in out-of-distribution (OOD) conditio…
Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing
Renjie Wang, Shangke Lyu, Xin Lang +2
Jumping constitutes an essential component of quadruped robots' locomotion capabilities, which includes dynamic take-off and adaptive landing. Existing quadrupedal jumping studies…
Robust Online Residual Refinement via Koopman-Guided Dynamics Modeling
Zhefei Gong, Shangke Lyu, Pengxiang Ding +2
Imitation learning (IL) enables efficient skill acquisition from demonstrations but often struggles with long-horizon tasks and high-precision control due to compounding errors. Re…
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only
Wei Xiao, Jiacheng Liu, Zifeng Zhuang +3
Improving the performance of pre-trained policies through online reinforcement learning (RL) is a critical yet challenging topic. Existing online RL fine-tuning methods require con…
Learning Robotic Policy with Imagined Transition: Mitigating the Trade-off between Robustness and Optimality
Wei Xiao, Shangke Lyu, Zhefei Gong +2
Existing quadrupedal locomotion learning paradigms usually rely on extensive domain randomization to alleviate the sim2real gap and enhance robustness. It trains policies with a wi…