DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity
Kang-Won Lee Yuzhe Qin Xiaolong Wang Soo-Chul Lim
The sense of touch is an essential ability for skillfully performing a
variety of tasks, providing the capacity to search and manipulate objects
without relying on visual information. Extensive research has been conducted
over time to apply these human tactile abilities to robots. In this paper, we
introduce a multi-finger robot system designed to search for and manipulate
objects using the sense of touch without relying on visual information.
Randomly located target objects are searched using tactile sensors, and the
objects are manipulated for tasks that mimic daily-life. The objective of the
study is to endow robots with human-like tactile capabilities. To achieve this,
binary tactile sensors are implemented on one side of the robot hand to
minimize the Sim2Real gap. Training the policy through reinforcement learning
in simulation and transferring the trained policy to the real environment, we
demonstrate that object search and manipulation using tactile sensors is
possible even in an environment without vision information. In addition, an
ablation study was conducted to analyze the effect of tactile information on
manipulative tasks. Our project page is available at
https://lee-kangwon.github.io/dextouch/