ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim
We present ZeroBot, a real2sim framework for learning a robot manipulation task from scratch in minutes under challenging conditions: zero human demonstrations, zero policy pre-training, and zero known object models. Given only a single view of an object and a goal pose for that object, ZeroBot uses image-to-3D generative models to obtain a complete object mesh, which is used in simulation for large-scale parallel reinforcement learning. To accelerate training, we introduce an action space which leverages the generated geometry and learned value function to sample states involving robot-object contact. When evaluated on real-world tasks including grasping, pushing, articulated object inte...
| Authors | Ivan Kapelyukh, Xiaohan Zhang, Stephen James et al. |
|---|---|
| Primary category | cs.RO |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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