5 papers · 1 filter
PreferenceFlow: Test-Time Guidance of Flow-Matching Robot Policies from Human Interventions
Yiqi Tang, Diyuan Shi, Runze Li +1
Flow-matching policies can represent complex robot behaviors but remain susceptible to local errors under distribution shift at deployment. Many reinforcement learning approaches t…
NFPO: Stabilized Policy Optimization of Normalizing Flow for Robotic Policy Learning
Diyuan Shi, Yiqi Tang, Zifeng Zhuang +1
Deep Reinforcement Learning (DRL) has experienced significant advancements in recent years and has been widely used in many fields. In DRL-based robotic policy learning, however, c…
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…
TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot Control
Zifeng Zhuang, Diyuan Shi, Runze Suo +5
Complex high-dimensional spaces with high Degree-of-Freedom and complicated action spaces, such as humanoid robots equipped with dexterous hands, pose significant challenges for re…
Unlock Reliable Skill Inference for Quadruped Adaptive Behavior by Skill Graph
Hongyin Zhang, Diyuan Shi, Zifeng Zhuang +6
Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics. Although some impressive…