  Human hands are capable of in-hand manipulation in the presence of different
hand motions. For a robot hand, harnessing rich tactile information to achieve
this level of dexterity still remains a significant challenge. In this paper,
we present AnyRotate, a system for gravity-invariant multi-axis in-hand object
rotation using dense featured sim-to-real touch. We tackle this problem by
training a dense tactile policy in simulation and present a sim-to-real method
for rich tactile sensing to achieve zero-shot policy transfer. Our formulation
allows the training of a unified policy to rotate unseen objects about
arbitrary rotation axes in any hand direction. In our experiments, we highlight
the benefit of capturing detailed contact information when handling objects
with varying properties. Interestingly, despite not having explicit slip
detection, we found rich multi-fingered tactile sensing can implicitly detect
object movement within grasp and provide a reactive behavior that improves the
robustness of the policy. The project website can be found at
https://maxyang27896.github.io/anyrotate/.
