so101-button-touching-policy-ideal-30 is an open-source imitation learning policy for robotics, trained to predict action chunks from teleoperated data. It is designed for robotics researchers and developers working on autonomous agents and control policies.
So101 Button Touching Policy Ideal 30 is an Other AI product. It focuses on providing an open-source policy for robotics action chunking using imitation learning. So101 Button Touching Policy Ideal 30 is an open-source project aimed at robotics researchers and developers. The project is open source (Apache-2.0). So101 Button Touching Policy Ideal 30 is available on the web, API, and the command line, and it can be self-hosted.
It is developed by GoldenPeel, and the product first shipped in 2024. The project is developed in the open on GitHub with 25.8k stars and 157 commits in the last 90 days. Across PulseGate's embedding index, So101 Button Touching Policy Ideal 30 has few near neighbours, marking it as relatively distinct. Among its 4 catalogued features are imitation learning, robotics policy, and action chunking.
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