5 papers · 1 filter
TRACE: Efficient Transfer Learning for Neural Whole-Body Control Across Humanoid Embodiments
Qingyu Lan, Zhihao Tian, Zifan Wang +1
Humanoid behavior foundation models (BFMs) enable diverse whole-body control, but adapting a pretrained controller to a new embodiment remains challenging because of kinematic and…
Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain
Zifan Wang, Yizhao Li, Teli Ma +5
Humanoid behavior foundation models aim to acquire reusable whole-body control policies from broad human motion priors, enabling a single controller to produce diverse and expressi…
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…
GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning
Yufei Jia, Heng Zhang, Ziheng Zhang +39
Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs in proprioception-based loco…
DiT4DiT: Jointly Modeling Video Dynamics and Actions for Generalizable Robot Control
Teli Ma, Jia Zheng, Zifan Wang +4
Vision-Language-Action (VLA) models have emerged as a promising paradigm for robot learning, but their representations are still largely inherited from static image-text pretrainin…