Gym-Anything for Robotics: Scaling physical simulations with digital agents

原文：Gym-Anything for Robotics: Scaling physical simulations with digital agents

作者：Pranjal Aggarwal, Sean Welleck

来源：arXiv cs.AI（人工智能）

正文

Computer Science > Robotics

arXiv:2610.40565v1 (cs)

[Submitted on 29 Oct 2026]

Title:Gym-Anything for Robotics: Scaling physical simulations with digital agents

Authors:Pranjal Aggarwal, Sean Welleck

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Abstract:Realistic, fast robotics simulations could enable large-scale training of embodied AI models that transfer to the real world. However, despite decades of progress, available simulations cover only a narrow range of real-world tasks and physical phenomena. The core bottleneck is that creating better simulations requires significant expert effort and therefore does not scale. To address this, we introduce Gym-Anything for Robotics, a framework in which coding agents create realistic robotics simulations. Our core insight is that simulation creation is a coding agent task, so improvements in coding agents translate directly into richer and more faithful simulations. We select simulations based on U.S. GDP data to cover economically valuable physical work, define what makes a good simulation and a good task, and develop an adversarial creation-audit loop that checks each simulation and task against these definitions. Using this pipeline, we build RoboWorld, a set of 25 simulations with 25 test and 175 training RL environments of varying difficulty. Even the best frontier models fail on every RoboWorld test task, achieving an average reward of only 0.06. Building on RoboWorld, we introduce several new ways for frontier models to improve, including in-context learning across repeated attempts, a privileged teacher that reads the simulator state and teaches a student in context, self-assisted learning from the model’s own privileged experience, and code policies that generate demonstrations across varied scenes to train vision-language-action models (VLAs) such as $\pi_{0.5}$. Each of these substantially improves performance. We release our pipeline, RL environments, and benchmark to facilitate future research. Code and data are available at this https URL

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:

arXiv:2610.40565 [cs.RO]

(or

arXiv:2610.40565v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.40565

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arXiv-issued DOI via DataCite

主题

机器人 · 人工智能 · 机器学习

由「前沿雷达」于 2026-10-30 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。