  Recent progress in reinforcement learning (RL) and tactile sensing has
significantly advanced dexterous manipulation. However, these methods often
utilize simplified tactile signals due to the gap between tactile simulation
and the real world. We introduce a sensor model for tactile skin that enables
zero-shot sim-to-real transfer of ternary shear and binary normal forces. Using
this model, we develop an RL policy that leverages sliding contact for
dexterous in-hand translation. We conduct extensive real-world experiments to
assess how tactile sensing facilitates policy adaptation to various unseen
object properties and robot hand orientations. We demonstrate that our 3-axis
tactile policies consistently outperform baselines that use only shear forces,
only normal forces, or only proprioception. Website:
https://jessicayin.github.io/tactile-skin-rl/
