  Tactile information plays a critical role in human dexterity. It reveals
useful contact information that may not be inferred directly from vision. In
fact, humans can even perform in-hand dexterous manipulation without using
vision. Can we enable the same ability for the multi-finger robot hand? In this
paper, we present Touch Dexterity, a new system that can perform in-hand object
rotation using only touching without seeing the object. Instead of relying on
precise tactile sensing in a small region, we introduce a new system design
using dense binary force sensors (touch or no touch) overlaying one side of the
whole robot hand (palm, finger links, fingertips). Such a design is low-cost,
giving a larger coverage of the object, and minimizing the Sim2Real gap at the
same time. We train an in-hand rotation policy using Reinforcement Learning on
diverse objects in simulation. Relying on touch-only sensing, we can directly
deploy the policy in a real robot hand and rotate novel objects that are not
presented in training. Extensive ablations are performed on how tactile
information help in-hand manipulation.Our project is available at
https://touchdexterity.github.io.
