arXiv Computer Vision By Meng Cao, Can Zhao, Jinjie Wei, Haonan Zhao, Yulong Zhang, Duo Han, Xiaofan Li, Ziwu Song, Rongtao Xu, Ruyi Gan, Hao Wang, Qian Wang, Daolin Ma, Ian Reid, Xiaodan Liang

EgoJEPA: Beyond Retargeting for Dexterous Manipulation from Egocentric Videos

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arXiv Computer Vision
1d ago

Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation

The paper introduces P³-WAM, a unified world action model for egocentric dexterous manipulation that integrates 3D perception, action-conditioned prediction, and goal-directed planning. It operates in three modes—Perceive, Predict, and Plan—using a mixture-of-transformers architecture that shares base weights and adapts to modality-specific inputs. Experiments demonstrate that unified training improves performance across all three capabilities, outperforming specialized baselines on 3D hand reconstruction and action-conditioned video prediction while producing more instruction‑adherent manipulation videos.

By Zirui Wu, Chen Guo, Tianjian Jiang, Zeren Jiang, Zeming Li, Lingchen Yang, Longteng Duan, Ying Xue, Handi Yin, Shenyuan Gao, Martin R. Oswald, Jun Zhang, Ping Tan, Jie Song
arXiv Machine Learning
Jun 10

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.

By Beomjun Kim, Seong Hyeon Park, Seunghoon Sim, Seungjun Moon, Sanghyeok Lee, Jinwoo Shin
arXiv Computer Vision
Sep 22

AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation

arXiv:2609.22332v1 Announce Type: cross Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act...

By Jiadi You, Qize Yu, Yue Chen, Minghong Cai, Zhide Zhong, Yuran Wang, Bowen Ping, Jiaqi Liang, Zhenhao Shen, Haodong Yan, Yinchuan Li, Ruihai Wu, Xiaojuan Qi, Yingcong Chen
Hugging Face Trending Papers
Aug 12

HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing

Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abundant egocentric videos of human hands offer a scalable alternative, the profound discrepancies in appearance, articulation, and camera viewpoints between human and robotic data raise significant challenges for co-training.

arXiv Computer Vision
Sep 21

GALA: Geometry-Aware Latent Action Modeling for Vision-Language-Action Model Pretraining across Embodiments

The paper introduces GALA, a Geometry-Aware Latent Action modeling framework that enhances image-based latent actions with 3D end‑effector motion. It proposes the Unified End‑effector Motion Representation (UEMR) to preserve fine‑grained motion while improving cross‑embodiment generalizability. Experiments show GALA effectively models generalizable fine‑grained motions across embodiments, achieving high success rates in RoboCasa-GR1 and real‑world tasks.

By Yichen Liu, Puzhen Yuan, Xiang Zhu, Yanjiang Guo, Jianyu Chen