11 papers
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…
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…
GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation
Teli Ma, Jia Zheng, Zifan Wang +3
Learning manipulation skills from human demonstration videos offers a promising path toward generalizable and interpretable robotic intelligence-particularly through the lens of ac…
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…
GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping
Teli Ma, Zifan Wang, Jiaming Zhou +2
Inferring affordable (i.e., graspable) parts of arbitrary objects based on human specifications is essential for robots advancing toward open-vocabulary manipulation. Current grasp…
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
Teli Ma, Jiaming Zhou, Zifan Wang +2
Developing robots capable of executing various manipulation tasks, guided by natural language instructions and visual observations of intricate real-world environments, remains a s…
Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation
Jiaming Zhou, Teli Ma, Kun-Yu Lin +3
Learning generalizable visual representations across different embodied environments is essential for effective robotic manipulation in real-world scenarios. However, the limited s…