1 citations · 1 across the 7 of their papers we have counts for
8 papers
MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
Jia Zheng, Teli Ma, Yudong Fan +3
World Action Models (WAMs) couple a video dynamics prior to the policy and have shown encouraging results on tabletop manipulation, but iterative denoising over high-dimensional vi…
OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation
Zehao Yu, Jiakun Zheng, Weiji Xie +4
Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, however, existing data source…
ReMoGen: Open-Vocabulary Motion Generation via LLM Reasoning and Physics-Aware Refinement
Jiakun Zheng, Ting Xiao, Shiqin Cao +3
Text-to-motion (T2M) generation aims to control the behavior of a target character via textual descriptions. Leveraging text-motion paired datasets, existing T2M models have achiev…
TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control
Weiji Xie, Jiakun Zheng, Jinrui Han +4
Recent advances in humanoid whole-body motion tracking have enabled the execution of diverse and highly coordinated motions on real hardware. However, existing controllers are comm…
KungfuBot2: Learning Versatile Motion Skills for Humanoid Whole-Body Control
Jinrui Han, Weiji Xie, Jiakun Zheng +4
Learning versatile whole-body skills by tracking various human motions is a fundamental step toward general-purpose humanoid robots. This task is particularly challenging because a…
Unsupervised Skill Discovery through Skill Regions Differentiation
Ting Xiao, Jiakun Zheng, Rushuai Yang +4
Unsupervised Reinforcement Learning (RL) aims to discover diverse behaviors that can accelerate the learning of downstream tasks. Previous methods typically focus on entropy-based…