4 papers
Learning Context-Aware Motion Priors for Humanoid Control
Yunyang Mo, Yi Gu, Yangchen Zhou +2
Motion priors provide powerful guidance for learning naturalistic humanoid behaviors. However, existing methods typically learn a general, task-agnostic prior from the entire refer…
Action-Effect Memory Pretraining for Robot Manipulation
Yijing Zhou, Qiwei Liang, Sitong Zhuang +5
We present AEM, an Action-Effect Memory pretraining framework for robot manipulation that learns compact temporal representations from vision-action history. Unlike prior robot rep…
OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
Yunyang Mo, Jian Li, Qiwei Wu +2
While reinforcement learning (RL) enables robots to acquire skills autonomously, its real-world deployment is severely limited by inefficient and unsafe exploration. Human-in-the-l…
MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy
Jiaxu Wang, Junhao He, Jingkai Sun +5
Learning real-world dynamics from visual observations is crucial for various domains. A common strategy is to calibrate simulators by estimating physical parameters, yet accuracy i…