Closing robotics’ “100,000-year data gap”

Robotics faces a different scaling challenge than large language models. As Ken Goldberg has described it, there is a “100,000-year data gap” between the vast datasets used to train language systems and the limited embodied data available for robots. While simulation, synthetic data, and teleoperation all play a role, researchers such as Sergey Levine emphasize the importance of real-world deploym

Sector: Electronic Labour | Confidence: 98%
Source: https://www.reddit.com/r/robotics/comments/1rg63zg/closing_robotics_100000year_data_gap/

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Council (3 models): Synthesis failed

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