  Bimanual manipulation is a longstanding challenge in robotics due to the
large number of degrees of freedom and the strict spatial and temporal
synchronization required to generate meaningful behavior. Humans learn bimanual
manipulation skills by watching other humans and by refining their abilities
through play. In this work, we aim to enable robots to learn bimanual
manipulation behaviors from human video demonstrations and fine-tune them
through interaction. Inspired by seminal work in psychology and biomechanics,
we propose modeling the interaction between two hands as a serial kinematic
linkage -- as a screw motion, in particular, that we use to define a new action
space for bimanual manipulation: screw actions. We introduce ScrewMimic, a
framework that leverages this novel action representation to facilitate
learning from human demonstration and self-supervised policy fine-tuning. Our
experiments demonstrate that ScrewMimic is able to learn several complex
bimanual behaviors from a single human video demonstration, and that it
outperforms baselines that interpret demonstrations and fine-tune directly in
the original space of motion of both arms. For more information and video
results, https://robin-lab.cs.utexas.edu/ScrewMimic/
