  Replicating human-like dexterity in robot hands represents one of the largest
open problems in robotics. Reinforcement learning is a promising approach that
has achieved impressive progress in the last few years; however, the class of
problems it has typically addressed corresponds to a rather narrow definition
of dexterity as compared to human capabilities. To address this gap, we
investigate piano-playing, a skill that challenges even the human limits of
dexterity, as a means to test high-dimensional control, and which requires high
spatial and temporal precision, and complex finger coordination and planning.
We introduce RoboPianist, a system that enables simulated anthropomorphic hands
to learn an extensive repertoire of 150 piano pieces where traditional
model-based optimization struggles. We additionally introduce an open-sourced
environment, benchmark of tasks, interpretable evaluation metrics, and open
challenges for future study. Our website featuring videos, code, and datasets
is available at https://kzakka.com/robopianist/
