DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity
This work addresses the challenge of vision-free robotic manipulation for applications in dark or occluded environments, representing an incremental advance in tactile-based robotics.
The paper tackles the problem of enabling robots to manipulate objects without vision by using tactile sensing, demonstrating that a multi-finger robot can perform blind manipulation tasks in the dark with reinforcement learning from simulation to real-world transfer.
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. In this paper, we introduce a multi-finger robot system designed to manipulate objects using the sense of touch, without relying on vision. For tasks that mimic daily life, the robot uses its sense of touch to manipulate randomly placed objects in dark. The objective of this study is to enable robots to perform blind manipulation by using tactile sensation to compensate for the information gap caused by the absence of vision, given the presence of prior information. Training the policy through reinforcement learning in simulation and transferring the trained policy to the real environment, we demonstrate that blind manipulation can be applied to robots without vision. In addition, the experiments showcase the importance of tactile sensing in the blind manipulation tasks. Our project page is available at https://lee-kangwon.github.io/dextouch/