RW RL Dataset: Real World Reinforcement Learning for Robots RW RL Dataset is a real world robot interaction dataset released by Boden Intelligence, Junpu Innovation Center, and the MINT Lab at Shanghai Jiao Tong University. It is designed for a bottleneck that imitation learning only datasets do not directly solve: how can robot policies keep improving in the physical world after they leave the ideal demonstration trajectory? The broader RW RL data program targets 1000+ hours of real robot interaction data across multiple robot embodiments, scene domains, task templates, and data regimes. In addition to teleoperated demonstrations, the dataset design emphasizes human intervention, autonomous rollouts, reward signals, done labels, and structured task semantics so that researchers can study policy correction, recovery, and closed loop improvement in real environments. The current Hugging Face repository contains the R1Lite release files listed below. The full RW RL program is designed as an expanding real robot RL data base, and future subsets can follow the same file layout. Dataset Coverage and Demo Scenario and embodiment coverage across robot series, scene domains, task templates…
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