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English(EN) Pre-training Visual Dexterity in Simulation

机器人研究探索预训练以增强灵巧性 · 已追踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文介绍了一种通过预训练提高机器人灵巧性的新颖框架。第一篇，ADEPT，利用在通用物体重定位任务上的强化学习来创建下游任务的先验知识，从而实现多指机器人上更快的学习和更好的性能。第二篇，Simulation Pre-training for Dexterity (SPD)，利用虚拟现实中的人类遥操作来收集用于预训练因果变换器的大型数据集，证明了模拟数据可以有效提高现实世界中的灵巧操作能力。 AI

影响 这些预训练框架可以显著加速能够执行复杂操作任务的灵巧机器人的开发和部署。

排序理由 arXiv上发表了两篇学术论文，介绍了机器人灵巧性预训练的新方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

机器人研究探索预训练以增强灵巧性 · 已追踪2个来源

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arXiv上发表了两篇学术论文，介绍了机器人灵巧性预训练的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jayjun Lee, Jessica Yin, Asif Rana, Nicholas Blauch, Sam Mady, Mohak Bhardwaj, Nima Fazeli, Nathan Ratliff, Karl Van Wyk, Ankur Handa ·

    ADEPT：通过强化学习的预训练和后训练加速灵活性

    arXiv:2608.19182v1 Announce Type: cross Abstract: We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF) robot embodiments that can solve…

  2. arXiv cs.AI TIER_1 English(EN) · Sarthak Kamat, Adam Rashid, Satvik Sharma, Aseem Doriwala, Chelsea Finn, Phillip Isola, C. Karen Liu ·

    在模拟中预训练视觉灵巧性

    arXiv:2608.15917v1 Announce Type: cross Abstract: Large-scale pre-training has made robot policy fine-tuning increasingly data-efficient, but this progress has largely been driven by datasets and embodiments built around simple parallel-jaw grippers. Dexterous, multi-fingered han…

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