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English(EN) SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale

SPARC框架提高了机器人演示标注的可靠性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SPARC，一个用于从机器人演示生成可靠空间标注的新框架。SPARC为每个标注分配一个置信度分数，解决了现有自动化流水线中标签噪声的问题。该框架利用机器人任务固有的时空结构来提高标注质量并保留更多有用数据。SPARC在物体定位和指向基准测试中取得了最先进的结果，并且使用其标注训练的策略在现实世界的复杂环境中表现更好。 AI

影响 提高了用于训练具身基础模型和机器人策略的数据的可靠性。

排序理由 这是一篇描述机器人学新框架和基准测试的研究论文。

在 arXiv cs.CV 阅读 →

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SPARC框架提高了机器人演示标注的可靠性

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Nils Blank, Paul Mattes, Maximilian Xiling Li, Jakub Suliga, Thomas Roth, Moritz Reuss, Pankhuri Vanjani, Rudolf Lioutikov ·

    SPARC：可扩展的机器人演示可靠空间标注

    arXiv:2606.13497v1 Announce Type: cross Abstract: This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with structured spatial annotations and assigns each an…

  2. arXiv cs.CV TIER_1 English(EN) · Rudolf Lioutikov ·

    SPARC：大规模机器人演示中的可靠空间标注

    This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with structured spatial annotations and assigns each annotation a reliability score. Structured spatial a…

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