DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
Authors: name: Hao-Shu Fang, name: Branden Romero, name: Yichen Xie, name: Arthur Hu, name: Bo-Ruei Huang, name: Juan Alvarez, name: Matthew Kim, name: Gabriel Margolis, name: Kavya Anbarasu, name: Masayoshi Tomizuka, name: Edward Adelson, name: Pulkit Agrawal

We introduce perioperation, a paradigm for robotic data collection that
sensorizes and records human manipulation while maximizing the transferability
of the data to real robots. We implement this paradigm in DEXOP, a passive hand
exoskeleton designed to maximize human ability to collect rich sensory (vision
+ tactile) data for diverse dexterous manipulation tasks in natural
environments. DEXOP mechanically connects human fingers to robot fingers,
providing users with direct contact feedback (via proprioception) and mirrors
the human hand pose to the passive robot hand to maximize the transfer of
demonstrated skills to the robot. The force feedback and pose mirroring make
task demonstrations more natural for humans compared to teleoperation,
increasing both speed and accuracy. We evaluate DEXOP across a range of
dexterous, contact-rich tasks, demonstrating its ability to collect
high-quality demonstration data at scale. Policies learned with DEXOP data
significantly improve task performance per unit time of data collection
compared to teleoperation, making DEXOP a powerful tool for advancing robot
dexterity. Our project page is at https://dex-op.github.io.