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

Abstract

Dexterous robotic hands perform complex, contact-rich manipulation. Imitation learning provides a route to such skills, but collecting human demonstrations with accurate hand actions and rich tactile information remains a key bottleneck. We present MILE, a teleoperation-based data-collection system comprising the wearable MILE exoskeleton and the mechanically corresponding MILE-Tac robotic hand. Because human-hand anatomy and wearability place tighter constraints on the high-DoF wearable, our human-first design begins with the MILE exoskeleton, equipped with custom modular joint encoders for accurate joint-angle acquisition. We then design the MILE-Tac robotic hand to share the exoskeleton's selected kinematic topology and joint-axis arrangement while satisfying robot-side implementation constraints, and equip its fingertips with compact visuotactile sensor modules. This correspondence enables direct exoskeleton-to-robot joint-space command transfer without online task-space inverse-kinematics retargeting. During teleoperation, the system synchronously records task-specific visual observations, four fingertip visuotactile streams, robot-hand proprioception, and exoskeleton-derived action commands. In a four-task teleoperation benchmark, MILE achieved a mean success rate of 76%, compared with 28% and 8% for glove-based and vision-based baselines, respectively. For downstream imitation learning, we trained paired ACT and DP policies with and without tactile input on MILE-collected demonstrations. The tactile-input variants achieved higher success rates in all paired evaluations.

Human-First Hardware Design

Teleoperation & Data Collection 

Data Collection Pipeline

Dataset Demonstration

Exoskeleton Precision Validation 

Autonomous In-Hand Manipulation

Autonomous 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.