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English(EN) RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

RynnValue 模型利用时间距离扩展机器人学习 · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出 RynnValue，这是一种用于机器人操作的开源价值基础模型，它利用时间距离作为监督目标。这种方法允许模型扩展到超过 7000 小时的数据，而无需偏好或进度注释。RynnValue 在 RBM-EVAL-OOD 基准测试上达到了 0.675 的 Kendall's tau_a，优于现有的最先进方法。当转换为密集奖励时，RynnValue 显著提高了现实世界策略的成功率，证明了时间距离对于通用机器人策略的有效性。 AI

影响 将时间距离确立为通用机器人策略的可扩展监督目标，有可能加速机器人学习的进展。

排序理由 该集群描述了一篇详细介绍机器人学习新模型和方法论的研究论文。

在 arXiv cs.LG 阅读 →

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RynnValue 模型利用时间距离扩展机器人学习 · 跟踪 2 个来源

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dongchi Huang, Hongyin Zhang, Bohan Hou, Siteng Huang, Zhian Su, Hang Guo, Tong Lu, Zhaofeng Xu, Jiahao Tang, Jianfei Yang, Donglin Wang, Peixi Peng, Mingxiu Chen, Deli Zhao, Xin Li ·

    RynnValue：利用时间距离扩展机器人价值基础模型

    arXiv:2608.09853v1 Announce Type: cross Abstract: General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored. Existing approaches tie…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    RynnValue：利用时间距离扩展机器人价值基础模型

    General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored. Existing approaches tie supervision to task-internal anchors such as pref…

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