policy_2026-07-16_shakeitup5_bench_notact is an open-source imitation-learning policy model for robotics, designed to predict short action chunks from teleoperated data. Distributed via Hugging Face, it is compatible with LeRobot and supports evaluation and fine-tuning for research in robotic control. It is intended for robotics researchers and developers working on advanced policy learning.
Policy 2026 07 16 Shakeitup5 Bench Notact is an Other AI product. It enables robotics researchers to apply imitation learning for chunked action prediction in robotic control tasks. It is built as an open-source project for robotics researchers. Policy 2026 07 16 Shakeitup5 Bench Notact is open source under the Apache-2.0 license. It runs on the web, API, and the command line.
jogarulfop builds and maintains Policy 2026 07 16 Shakeitup5 Bench Notact, and the product first shipped in 2024. Development happens publicly on GitHub with 25.9k stars and 158 commits in the last 90 days. Key capabilities include imitation learning, action chunking, and robotics policy.
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