English(EN) How Should a Simulation-to-Reality Transfer Budget Be Spent?

机器人学习研究表明，系统识别比广泛随机化更受重视

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-26 04:00

一篇新研究论文探讨了机器人学习中仿真到现实迁移的最佳资源分配。研究表明，将一小部分现实世界测量时间用于系统识别，比广泛的域随机化更有效。研究结果表明，即使有限的真实数据用于参数估计，也能显著提高策略性能，优于在包含真实系统参数的更广泛随机化范围内训练的策略。 AI

影响提出了一种更有效的仿真到现实迁移方法，可能减少机器人系统对现实世界数据收集的需求。

排序理由 arXiv上发表的研究论文，详细介绍了机器人学习的发现。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; arXiv上发表的研究论文，详细介绍了机器人学习的发现。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Syed Hamzah Rizvi, Yash Vardhan Tomar · 2026-06-26 04:00

模拟到现实迁移预算应如何分配？

arXiv:2606.22062v2 Announce Type: replace-cross Abstract: Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynam…