EgoJEPA is a three‑stage framework that learns embodiment‑agnostic representations from egocentric human videos, eliminating the need for hand‑crafted retargeting mappings. It first pre‑trains latent actions via visual prediction conditioned on hand‑motion differences, then models future visual dynamics and latent actions jointly, and finally adapts to specific robots with lightweight action adapters. Experiments on five manipulation tasks across three robot embodiments show a 74% average success rate versus 65% for a retargeting baseline, while cutting cross‑embodiment wall‑clock cost by 47.2%.
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
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
arXiv:2606. 00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models.
By Zhiyuan Feng, Qixiu Li, Huizhi Liang, Rushuai Yang, Yichao Shen, Zhiying Du, Zhaowei Zhang, Yu Deng, Li Zhao, Hao Zhao, Zongqing Lu, Oier Mees, Marc Pollefeys, Jiaolong Yang, Baining Guo
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:2606. 08530v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments.
By Yuan Zhang, Shiqi Zhang, Yedong Shen, Shuai Dong, Jiajun Deng, Xin Zhang, Yuxuan Gao, Jiajia Wu, Xin Nie, Zhiyuan Cheng, Jianmin Ji, Yanyong Zhang, Xingyi Zhang, Jia Pan
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
EgoLAP is a VLA pre‑training framework that learns from both human and robot trajectories by expressing motion intent as temporally abstracted language actions linked to motion‑level reasoning grounded in scene geometry, physics, and object affordances. It jointly trains on a shared language‑based action chain‑of‑thought, enabling the transfer of human experience to robot control. In extensive real‑world and simulated experiments, EgoLAP achieves 80.1% mean real‑world task progress, a 2.3× improvement over alternative action representations, and outperforms composite reasoning formats.
By Lihan Zha, Shresth Grover, Tenny Yin, Samuel M. Bateman, Hengkai Pan, Mengchao Zhang, Aykut Onol, Allen Z. Ren, Dhruv Shah, Anirudha Majumdar
arXiv:2608. 14028v1 Announce Type: cross Abstract: Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spaces vary across embodiments.
By Zhiyue Zhao, Jingyi Wu, Hairuo Liu, Mingyu Liu, Liyang Li, Hengdi Zhang, Tong He, Zhengxue Cheng
AtomEgo investigates how to integrate large-scale egocentric human interaction data into embodied foundation model pre‑training. The study uses a curated 2,659‑hour corpus and a scalable data pipeline to evaluate three co‑training paradigms across vision‑language‑action and world‑action architectures. Results show that the benefit of egocentric data depends on both its scale and the quality of alignment with robotic embodiment, offering practical guidance for scalable ego‑robot pre‑training.
By Di Wu, Dongchen Zheng, Junhe Sheng, Zhongxing Wei, Songxin Zhang, Zejian Xie, Xiaoquan Sun, Junyang Zheng, Zhuoyang Song, Jiaxing Zhang, Jiayu Chen
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for control. Our key insi...
Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot embodiments. However, achieving significant cross-embodiment transfer is often still challenging.
arXiv:2606. 11628v1 Announce Type: cross Abstract: The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments.
By Harsh Gupta, Guanya Shi, Wenzhen Yuan