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

Paper recorded by Signals 4 on 2026-10-29 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.AI · 人工智能 · first seen 2026-10-30

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 int

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#72 most recent of 920 cs.AI papers we have recorded · ↑ newer: VeriCUA: Verification as Agentic Exploration for Computer-Using Agents · ↓ older: Comparing Human Role-Players and LLM-Simulated Clients in Online Couns
Cite this page: Gym-Anything for Robotics: Scaling physical simulations with digital agents: the #72 most recent of 920 cs.AI papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/gym-anything-for-robotics-scaling-physical-simulations-with-digital-agents.html
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