Can AI teach robots with smarter practice scenes?

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MIT's Steerable Scene Generation uses diffusion AI to craft training worlds for robots. It creates diverse, physically accurate 3D scenes with everyday objects and is trained on more than 44 million scenes across tables, shelves, counters, and rooms. The system uses Monte Carlo Tree Search and reinforcement learning to steer scenes toward specific training goals, while respecting gravity and avoiding object overlaps. In tests, it achieved 98% shelf accuracy and 86% on table rearrangements. These virtual worlds train robots to place cutlery, organize food, and open jars, helping skills transfer to real deployments. Demos show robots in messy, realistic spaces, a leap from old, static simulations. This research could speed up safe, real-world deployment of autonomous systems while inviting policy and ethics discussions.
#AI #Robotics #DiffusionModels #SteerableScenes #MIT
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