  One of the key arguments for building robots that have similar form factors
to human beings is that we can leverage the massive human data for training.
Yet, doing so has remained challenging in practice due to the complexities in
humanoid perception and control, lingering physical gaps between humanoids and
humans in morphologies and actuation, and lack of a data pipeline for humanoids
to learn autonomous skills from egocentric vision. In this paper, we introduce
a full-stack system for humanoids to learn motion and autonomous skills from
human data. We first train a low-level policy in simulation via reinforcement
learning using existing 40-hour human motion datasets. This policy transfers to
the real world and allows humanoid robots to follow human body and hand motion
in real time using only a RGB camera, i.e. shadowing. Through shadowing, human
operators can teleoperate humanoids to collect whole-body data for learning
different tasks in the real world. Using the data collected, we then perform
supervised behavior cloning to train skill policies using egocentric vision,
allowing humanoids to complete different tasks autonomously by imitating human
skills. We demonstrate the system on our customized 33-DoF 180cm humanoid,
autonomously completing tasks such as wearing a shoe to stand up and walk,
unloading objects from warehouse racks, folding a sweatshirt, rearranging
objects, typing, and greeting another robot with 60-100% success rates using up
to 40 demonstrations. Project website: https://humanoid-ai.github.io/
