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Human Data. Robot Data. Virtual Data. BrainCo Dexterous Data Matrix connects diverse data sources to establish a closed-loop data ecosystem, enabling robots to learn from real-world experiences and achieve more natural interactions. #BrainCo #EmbodiedAI #FutureRobotics

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Tulsi Soni

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67,586 просмотров • 4 дней назад •via X (Twitter)

Наука и технологии#BrainCo#EmbodiedAI#FutureRobotics
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So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)
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Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵
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Robot-free demos can be collected through our VR interface, inspected, trained, and evaluated in a closed loop. Cool thing is that a small amount of real-robot data mixed with large-scale robot-free data can reach comparable performance, while reducing real-robot data needs by up to 20x. Code: Paper:
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