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PlayWorld: Learning Robot World Models from Autonomous Play

robot-playworld.github.io · 597 words · saved by 1 readers

PlayWorld: Learning Robot World Models from Autonomous Play

We introduce PlayWorld, a scalable framework for training high-fidelity Action-Conditioned Robot World Models using Autonomous Play. Instead of relying on success-biased human demonstrations, it continuously gathers diverse, contact-rich interactions and failure cases that better match downstream policy behaviors. The resulting model enables accurate dynamics prediction, reliable policy evaluation, and effective world-model-based RL fine-tuning that improves real-world performance. Collecting Play Data 🧱 Blocks 🥕 Carrot 👕 Towel System overview for autonomous play-data collection.…

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