MILE: A Mechanically Isomorphic Exoskeleton Data Collection System with Fingertip Visuotactile Sensing for Dexterous Manipulation

• URL: http://arxiv.org/abs/2512.00324v1
• Date: Sat, 29 Nov 2025 05:34:39 GMT
• Title: MILE: A Mechanically Isomorphic Exoskeleton Data Collection System with Fingertip Visuotactile Sensing for Dexterous Manipulation
• Authors: Jinda Du, Jieji Ren, Qiaojun Yu, Ningbin Zhang, Yu Deng, Xingyu Wei, Yufei Liu, Guoying Gu, Xiangyang Zhu,
• Abstract summary: Existing data-collection pipelines suffer from inaccurate motion manipulation, low data-collection efficiency, and missing high-resolution tactile sensing.<n>We address this gap with MILE, a mechanically tele-operation and data-collection system co-designed from human hand to robotic hand.
• Score: 17.138615434309575
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Imitation learning provides a promising approach to dexterous hand manipulation, but its effectiveness is limited by the lack of large-scale, high-fidelity data. Existing data-collection pipelines suffer from inaccurate motion retargeting, low data-collection efficiency, and missing high-resolution fingertip tactile sensing. We address this gap with MILE, a mechanically isomorphic teleoperation and data-collection system co-designed from human hand to exoskeleton to robotic hand. The exoskeleton is anthropometrically derived from the human hand, and the robotic hand preserves one-to-one joint-position isomorphism, eliminating nonlinear retargeting and enabling precise, natural control. The exoskeleton achieves a multi-joint mean absolute angular error below one degree, while the robotic hand integrates compact fingertip visuotactile modules that provide high-resolution tactile observations. Built on this retargeting-free interface, we teleoperate complex, contact-rich in-hand manipulation and efficiently collect a multimodal dataset comprising high-resolution fingertip visuotactile signals, RGB-D images, and joint positions. The teleoperation pipeline achieves a mean success rate improvement of 64%. Incorporating fingertip tactile observations further increases the success rate by an average of 25% over the vision-only baseline, validating the fidelity and utility of the dataset. Further details are available at: https://sites.google.com/view/mile-system.

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