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English(EN) TacSushi: Tactile-Grounded World-Action Modeling for Dexterous Sushi Manipulation

机器人通过触觉反馈学习灵巧寿司操作

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TacSushi，一种新颖的世界-动作策略，它利用触觉反馈来改进机器人对寿司等可变形物体的操作。该系统基于Cosmos3模型构建，从记录的未来后果中学习，并通过逐特征门控融合机制整合指尖触觉数据。在实际试验中，与缺乏触觉感知或未来后果监督的方法相比，TacSushi在分布内和分布外任务中均表现出显著更高的成功率。 AI

影响 增强了机器人对复杂、可变形物体的操作能力，有可能改善食品制备和其他精细任务的自动化。

排序理由 详细介绍新颖AI模型及其应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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机器人通过触觉反馈学习灵巧寿司操作

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详细介绍新颖AI模型及其应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Haodi Hu, Kaen Kogashi, Toshiaki Koike-Akino ·

    TacSushi：用于灵巧寿司操作的触觉地面世界-动作建模

    arXiv:2609.19613v1 Announce Type: cross Abstract: Dexterous food manipulation requires control under deformation, occlusion, and uncertain contact. We present TacSushi, a tactile-grounded, Cosmos3-based world-action policy that learns from recorded future consequences while actin…

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