MILE: A Mechanically Isomorphic Hand Exoskeleton and Visuotactile Robotic Hand for Data Collection in Dexterous Manipulation

Abstract

Dexterous robotic hands are expected to perform complex, contact-rich object manipulation, but learning such skills remains challenging because high-dimensional hands require high-fidelity demonstrations. Imitation learning provides a practical route for acquiring dexterous manipulation skills from human demonstrations, yet collecting synchronized multimodal demonstrations with accurate hand actions and tactile observations remains a key bottleneck. We present MILE, a teleoperation-based data-collection system comprising the human-first MILE exoskeleton and the mechanically corresponding MILE-Tac robotic hand. The system integrates custom-designed and fabricated modular joint encoders and compact MILE fingertip visuotactile sensor modules. The exoskeleton is informed by human-hand anatomy and ergonomic constraints, while the robotic hand is co-designed to preserve the selected four-finger kinematic topology. This correspondence enables joint-space command transfer and reduces reliance on task-space IK-based retargeting. The system synchronously records task-specific visual observations, four fingertip visuotactile streams, robot-hand proprioception, and exoskeleton-derived action commands. We compare MILE with glove- and vision-based teleoperation interfaces on four in-hand manipulation tasks and evaluate ACT- and DP-based policies with and without fingertip tactile input. We further evaluate autonomous policy deployment with the MILE-Tac hand mounted on a robotic arm. 

Human-First Hardware Design

co-designed from human hand to exoskeleton to robotic hand

Encoder Precision Test

Teleoperation Demonstration

Data Collection Pipeline

Dataset Demonstration

Inference & Ablation Experiment

In-Hand Manipulation

Arm-Hand Manipulation

BibTex

Acknowledgement

The authors thank Yunfan Zhang, Qianyou Zhao, Yongyao Li, Xu Song, and Zheng Wang for advice on hardware design and learning policies; and Longyan Wu, Yueshi Dong, Jiapeng He, Nianzu Lv, Yuxuan Wu, Yutong Pei, Jinnuo Zhang, Zhenle Liu, Boyang Peng, and Junjie Xia for assistance with data collection, the user study, and hardware renderings.