HumanEgo is a framework that enables zero‑shot robot learning from short egocentric human videos by converting each demonstration into an entity‑level hand‑object interaction representation and training a flow‑matching policy with dense auxiliary objectives. The method is robot‑data‑free, hardware‑agnostic, and data‑efficient, achieving 92.5 % success on four real‑world tasks with only 30 minutes of human video per task and outperforming matched‑time robot teleoperation by 41 %. HumanEgo also robustly transfers zero‑shot across new robots, cameras, and environments, and is released as an open‑source tool for learning robot policies directly from human data.
By Zhi Wang, Botao He, Kelin Yu, Seungjae Lee, Ruohan Gao, Furong Huang, Yiannis Aloimonos
arXiv:2606. 06627v1 Announce Type: cross Abstract: Human video datasets used for cotraining robot manipulation policies largely consist of curated demonstrations where motions are orchestrated to resemble robot behavior and 3D hand poses are captured with specialized hardware.
By Richard Li, Aditya Prakash, Andrew Wen, Saurabh Gupta, Yilun Du, Pulkit Agrawal
arXiv:2608. 07154v1 Announce Type: cross Abstract: Open-source robotics and foundation models have lowered the barrier to embodied AI, yet language-guided laboratory automation still requires reliable alignment from instructions and observations to safe actions.
By Yang Shen, Chonghao Cheng, Ziyi Zhao, Jialuo Zhu, Zhenyi Yi, Qi Zhao, Jian Yang, Yuhui Shi, Chin-Teng Lin
arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.
By Allison Andreyev, Landon Eum, Nestor Tiglao, Romel Gomez
RoboTok is an internet‑scale data engine that retrieves human manipulation videos from the web to train dexterous robot policies. It learns a latent motion space from 3D hand trajectories in actor‑centered reference frames, allowing manipulation behaviors to be compared across different viewpoints, scenes, and occlusions while remaining compact for efficient search. Experiments show RoboTok retrieves more relevant demonstrations and improves downstream robot task success compared to existing retrieval methods.
By Howard Qian, Yiting Chen, Yunfei Xie, Kejia Ren, Podshara Chanrungmaneekul, Gaotian Wang, Bowen Wen, Chen Wei, Kaiyu Hang
arXiv:2610.00981v1 Announce Type: cross
Abstract: We focus on language-conditioned flow-based manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic, motion-centric repr...
By Shota Kobayashi, Koki Seno, Daichi Yashima, Komei Sugiura
arXiv:2609.12541v1 Announce Type: new
Abstract: We demonstrate that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-spec...
By Mengzhao Jia, Yang Lin, Xixin Zhang, Zhihan Zhang, Xiaobai Liu, Meng Jiang
arXiv:2606. 10244v1 Announce Type: cross Abstract: We introduce Yielding Universal Bidigital Interface (YUBI), a finger-aligned gripper designed to enable intuitive, ergonomic, and scalable data collection for bimanual dexterous manipulation.
By Takehiko Ohkawa, Jumpei Arima, Yuki Noguchi, Masatoshi Tateno, Makoto Sugiura, Takuya Okubo, Kengo Ikeuchi, Yuma Shin, Hiroki Nishizawa, Naoaki Kanazawa, Yuki Wakayama, Daiki Fukunaga, Koshi Makihara, Tomohiro Motoda, Floris Erich, Yukiyasu Domae, Tatsuya Matsushima, Yohishiro Okumatsu, Kei Ota
Ensuring reliability in uncertain environments remains difficult for long-horizon robotic manipulation. End-to-end VLA models are data-heavy and opaque, making diagnosis and verification difficult. Hi...
arXiv:2609.24660v2 Announce Type: cross
Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot...
By Shengcheng Luo, Xiaoyang Cheng, Hong Ying, Xiaoying Zhou, Jiaming Jiang, Haoran Guo, Wanlin Li, Ziyuan Jiao, Chenxi Xiao
Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness. We present NebulaVLA...