9 papers
LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models
Zhenhao Shen, Jiaqi Liang, Jasper Lu +11
Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to teleoperated robot data. Howe…
TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
Jielin Wu, Shenzhe Yao, Guanqi He +6
Human hand-object demonstrations provide dense reference motions for training dexterous manipulation reinforcement learning (RL) policies through reference tracking. However, to us…
OMG: Omni-Modal Motion Generation for Generalist Humanoid Control
Siqiao Huang, Kun-Ying Lee, Dongming Qiao +5
Humanoid whole-body control has made significant progress in recent years, yet existing approaches remain limited to few-skill policies with heavy reward engineering, or motion tra…
Hold My Beer: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control
Yitang Li, Yuanhang Zhang, Wenli Xiao +5
Can your humanoid walk up and hand you a full cup of beer, without spilling a drop? While humanoids are increasingly featured in flashy demos like dancing, delivering packages, tra…
Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning
Nikhil Sobanbabu, Guanqi He, Tairan He +2
Sim-to-real discrepancies hinder learning-based policies from achieving high-precision tasks in the real world. While Domain Randomization (DR) is commonly used to bridge this gap,…
ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills
Tairan He, Jiawei Gao, Wenli Xiao +15
Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a s…