  Bimanual manipulation with tactile feedback will be key to human-level robot
dexterity. However, this topic is less explored than single-arm settings,
partly due to the availability of suitable hardware along with the complexity
of designing effective controllers for tasks with relatively large state-action
spaces. Here we introduce a dual-arm tactile robotic system (Bi-Touch) based on
the Tactile Gym 2.0 setup that integrates two affordable industrial-level robot
arms with low-cost high-resolution tactile sensors (TacTips). We present a
suite of bimanual manipulation tasks tailored towards tactile feedback:
bi-pushing, bi-reorienting and bi-gathering. To learn effective policies, we
introduce appropriate reward functions for these tasks and propose a novel
goal-update mechanism with deep reinforcement learning. We also apply these
policies to real-world settings with a tactile sim-to-real approach. Our
analysis highlights and addresses some challenges met during the sim-to-real
application, e.g. the learned policy tended to squeeze an object in the
bi-reorienting task due to the sim-to-real gap. Finally, we demonstrate the
generalizability and robustness of this system by experimenting with different
unseen objects with applied perturbations in the real world. Code and videos
are available at https://sites.google.com/view/bi-touch/.
