EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos

• URL: http://arxiv.org/abs/2507.12440v3
• Date: Fri, 18 Jul 2025 07:18:39 GMT
• Title: EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
• Authors: Ruihan Yang, Qinxi Yu, Yecheng Wu, Rui Yan, Borui Li, An-Chieh Cheng, Xueyan Zou, Yunhao Fang, Xuxin Cheng, Ri-Zhao Qiu, Hongxu Yin, Sifei Liu, Song Han, Yao Lu, Xiaolong Wang,
• Abstract summary: In this paper, we explore training Vision-Language-Action (VLA) models using egocentric human videos.<n>With a VLA trained on human video that predicts human wrist and hand actions, we can perform Inverse Kinematics and convert the human actions to robot actions.<n>We propose a simulation benchmark called Ego Humanoid Manipulation Benchmark, where we design diverse bimanual manipulation tasks with demonstrations.
• Score: 49.820119587446655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real robot data collection for imitation learning has led to significant advancements in robotic manipulation. However, the requirement for robot hardware in the process fundamentally constrains the scale of the data. In this paper, we explore training Vision-Language-Action (VLA) models using egocentric human videos. The benefit of using human videos is not only for their scale but more importantly for the richness of scenes and tasks. With a VLA trained on human video that predicts human wrist and hand actions, we can perform Inverse Kinematics and retargeting to convert the human actions to robot actions. We fine-tune the model using a few robot manipulation demonstrations to obtain the robot policy, namely EgoVLA. We propose a simulation benchmark called Ego Humanoid Manipulation Benchmark, where we design diverse bimanual manipulation tasks with demonstrations. We fine-tune and evaluate EgoVLA with Ego Humanoid Manipulation Benchmark and show significant improvements over baselines and ablate the importance of human data. Videos can be found on our website: https://rchalyang.github.io/EgoVLA

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