  Achieving human-like dexterous manipulation remains a crucial area of
research in robotics. Current research focuses on improving the success rate of
pick-and-place tasks. Compared with pick-and-place, throw-catching behavior has
the potential to increase picking speed without transporting objects to their
destination. However, dynamic dexterous manipulation poses a major challenge
for stable control due to a large number of dynamic contacts. In this paper, we
propose a Stability-Constrained Reinforcement Learning (SCRL) algorithm to
learn to catch diverse objects with dexterous hands. The SCRL algorithm
outperforms baselines by a large margin, and the learned policies show strong
zero-shot transfer performance on unseen objects. Remarkably, even though the
object in a hand facing sideward is extremely unstable due to the lack of
support from the palm, our method can still achieve a high level of success in
the most challenging task. Video demonstrations of learned behaviors and the
code can be found on the supplementary website.
