VideoDex: Learning Dexterity from Internet Videos
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Abstract (arXiv)
To build general robotic agents that can operate in many environments, it is often imperative for the robot to collect experience in the real world. However, this is often not feasible due to safety, time, and hardware restrictions. We thus propose leveraging the next best thing as real-world experience: internet videos of humans using their hands. Visual priors, such as visual features, are often learned from videos, but we believe that more information from videos can be utilized as a stronger prior. We build a learning algorithm, VideoDex, that leverages visual, action, and physical priors from human video datasets to guide robot behavior. These actions and physical priors in the neural network dictate the typical human behavior for a particular robot task. We test our approach on a robot arm and dexterous hand-based system and show strong results on various manipulation tasks, outperforming various state-of-the-art methods. Videos at https://video-dex.github.io
Key facts. Cited by 101 papers in the map (data through 7 Oct 2026). Builds on 47 of its 73 references that are in the map. First posted on arXiv 8 Dec 2022.
Builds on
47 of 73 references.
References · 47
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Citing papers per year: 2023: 17; 2024: 30; 2025: 34; 2026: 20.
101 papers.
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