English(EN) ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation

ControlTac框架利用物理先验生成逼真的触觉图像

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了ControlTac，一个用于生成逼真触觉图像的新颖框架。该方法采用两阶段过程，从单个参考触觉图像开始，将图像合成条件化为接触力和姿态等物理因素。通过将生成过程与这些物理先验相结合，ControlTac产生的触觉信号比以前的模拟或自由形式生成技术更准确。实验表明，使用ControlTac增强的数据集可以提高机器人下游任务（如物体插入和模仿学习）的性能。 AI

影响增强了机器人研究和开发中合成触觉数据的真实性和实用性。

排序理由这是一篇详细介绍合成数据生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh, Cornelia Ferm\"uller, Yiannis Aloimonos, Ruohan Gao · 2026-09-10 04:00

ControlTac：通过物理控制的触觉图像生成来扩展触觉数据

arXiv:2505.20498v3 Announce Type: replace-cross Abstract: Vision-based tactile sensing is widely used in perception, reconstruction, and robotic manipulation, yet collecting large-scale tactile data remains costly due to diverse sensor-object interactions and inconsistencies acro…

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ControlTac：通过物理控制的触觉图像生成来扩展触觉数据

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