9 papers
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…
Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization
Jiaming Zhou, Ke Ye, Jiayi Liu +6
The generalization capabilities of vision-language-action (VLA) models to unseen tasks are crucial to achieving general-purpose robotic manipulation in open-world settings. However…
End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy
Zifan Wang, Xun Yang, Jianzhuang Zhao +5
The deployment of humanoid robots in unstructured, human-centric environments requires navigation capabilities that extend beyond simple locomotion to include robust perception, pr…