  In-hand object reorientation has been a challenging problem in robotics due
to high dimensional actuation space and the frequent change in contact state
between the fingers and the objects. We present a simple model-free framework
that can learn to reorient objects with both the hand facing upwards and
downwards. We demonstrate the capability of reorienting over 2000 geometrically
different objects in both cases. The learned policies show strong zero-shot
transfer performance on new objects. We provide evidence that these policies
are amenable to real-world operation by distilling them to use observations
easily available in the real world. The videos of the learned policies are
available at: https://taochenshh.github.io/projects/in-hand-reorientation.
