17 citations · 37 across the 22 of their papers we have counts for
5 papers · 1 filter
GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
AgiBot Research Team, Renhang Liu, Wenzhi Zhao +42
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video…
Efficient Camera Pose Augmentation for View Generalization in Robotic Policy Learning
Sen Wang, Huaiyi Dong, Jingyi Tian +7
Prevailing 2D-centric visuomotor policies exhibit a pronounced deficiency in novel view generalization, as their reliance on static observations hinders consistent action mapping a…
DynaRend: Learning 3D Dynamics via Masked Future Rendering for Robotic Manipulation
Jingyi Tian, Le Wang, Sanping Zhou +3
Learning generalizable robotic manipulation policies remains a key challenge due to the scarcity of diverse real-world training data. While recent approaches have attempted to miti…
FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation
Sen Wang, Le Wang, Sanping Zhou +4
Robotic manipulation in high-precision tasks is essential for numerous industrial and real-world applications where accuracy and speed are required. Yet current diffusion-based pol…
Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
Ye Niu, Sanping Zhou, Yizhe Li +2
In many complex scenarios, robotic manipulation relies on generative models to estimate the distribution of multiple successful actions. As the diffusion model has better training…