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Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation
Zirui Wu, Chen Guo, Tianjian Jiang +11
We introduce P-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and goal-directed planning th…
MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models
Hanyang Yu, Haitao Lin, Jingbo Zhang +4
World Action Models (WAMs) present a promising paradigm for robotic control via video prediction. However, current WAMs suffer from fundamental spatial bottlenecks: standard text i…
Universal Pose Pretraining for Generalizable Vision-Language-Action Policies
Haitao Lin, Hanyang Yu, Jingshun Huang +5
Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-spec…
Universal Features Guided Zero-Shot Category-Level Object Pose Estimation
Wentian Qu, Chenyu Meng, Heng Li +6
Object pose estimation, crucial in computer vision and robotics applications, faces challenges with the diversity of unseen categories. We propose a zero-shot method to achieve cat…