ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim

Abstract: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 interaction, and multi-stage manipulation, ZeroBot achieves an 87% success rate with an average training time of 119 seconds. These results show the value of using image-to-3D models in a real2sim framework for rapid, autonomous robot learning.

IEEE RA-Letters 2026. Project page: this https URL

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.34010 [cs.RO]

(or arXiv:2609.34010v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.34010 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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