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English(EN) SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception

SymmGrid框架通过并行对称性加速机器人上的学习

研究人员开发了SymmGrid,一个旨在显著加速深度强化策略机器人上学习的新框架。通过利用马尔可夫决策过程中的并行对称性,SymmGrid创建了一个几何网格结构,用多样化且一致的经验填充回放缓冲区。该方法在真实机器人操作任务的训练收敛速度和成功率方面取得了显著的改进,使机器人上的学习能够在几分钟内完成。 AI

影响 加速机器人上的学习,可能实现更快的机器人系统开发和部署。

排序理由 该项目是一篇详细介绍新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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SymmGrid框架通过并行对称性加速机器人上的学习

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该项目是一篇详细介绍新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Gabe Everett, Brice Gunter, Ryan Vander Stelt, Cleiver Ruiz-Martinez, Blake Hull, Juan Rojas ·

    SymmGrid:通过并行对称和自我中心-外中心视觉感知实现机器人学习的超大规模扩展

    arXiv:2607.26985v1 Announce Type: cross Abstract: Deep reinforcement policy learning directly in physical robots (on-robot learning) remains bottlenecked by slow wall-clock training times. We present SymmGrid, a trajectory level augmentation framework inspired by parallelized sym…