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.
arXiv:2609.37181v1 Announce Type: cross Abstract: Human demonstrations capture diverse scenes and rich whole-body skills without requiring robot teleoperation. Prior work on egocentric transfer has e...
arXiv:2610.18950v1 Announce Type: cross Abstract: Humanoid loco-manipulation requires coordinated whole-body interaction with the physical world, yet collecting robot demonstrations at scale remains...
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.
arXiv:2611.05163v2 Announce Type: replace-cross Abstract: Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleopera...
arXiv:2606. 28813v1 Announce Type: cross Abstract: Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions.
arXiv:2607. 08436v1 Announce Type: cross Abstract: Egocentric human data offers scalable supervision for robot manipulation.
The paper introduces RoboTryOn, a method that transforms human egocentric videos into synthetic demonstrations for robots by virtually swapping the human with a target robot while preserving task, objects, and background. It uses autoregressive keyframe translation guided by human hand skeletons and an inverse dynamics model to generate action labels for policy learning. Experiments on various humanoid robots show that policies trained on these synthetic videos achieve up to 60% success on new tasks, matching teleoperated demonstration performance at roughly one‑third the cost.
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.
arXiv:2510. 08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities.
arXiv:2610.33685v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are bottlenecked by the scarcity of robot data. Egocentric human manipulation videos offer an abundant, diverse,...
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.
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.