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You can actually interact with the world simulator directly in the browser. 🤖 Here is a quick screen recording (8x speed) of me playing with it: real-time action-conditioned video prediction across rigid objects, deformable objects, rope, and object piles. Try it yourself (no install required): https://t.co/rqAmgzcB7F Huge kudos to my student @YXWangBot for making the interactive demo happen!
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"text": "For a long time, I was skeptical about action-conditioned video prediction for robotics. Many models look impressive, but once you ask them to handle long-horizon manipulation with real physical interaction, things quickly fall apart (e.g., Genie is amazing but mostly focused on navigation).\n\nThis project changed my mind.\n\nI'm beyond excited to share Interactive World Simulator, a project we have been working on for the past ~1.5 years 🤖\n\nOne of the first world models that produces convincing results for long-horizon robotic manipulation involving complex physical interactions, across a diverse range of objects (rigid objects, deformables, ropes, object piles). It directly unlocks scalable data generation for robotic policy training and policy evaluation.\n\nTry it yourself (no installation needed): https://t.co/u1ZP34yVET\nPlay directly with the simulator in your browser.\n\nKey Takeaways:\n\n1️⃣ 15 Hz long-horizon action-conditioned video prediction for 10+ minutes on a single RTX 4090 GPU\n\n2️⃣ Visual and dynamic fidelity: people often ask how much sim data equals one real data point. In our experiments, it turns out to be close to one-to-one using the Interactive World Simulator\n\n3️⃣ Stress testing matters: we emphasize interactive stress testing to understand robustness and stability and to build trust in the simulator\n\n4️⃣ The model is trained with only ~6 hours of real-world random interaction data on a single GPU. Imagine what happens if we scale this 1000× or even 1M×\n\nHuge credit to @YXWangBot, who led this effort with countless hours of work on data collection, training recipes, and system design. I'm incredibly proud of the work he did here!\n\nEnjoy the demos and videos. We also fully open-sourced the codebase for anyone interested in applying this to their own tasks.\n\n#Robotics #RobotLearning #WorldModels #EmbodiedAI",
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