  While humans can use parts of their arms other than the hands for
manipulations like gathering and supporting, whether robots can effectively
learn and perform the same type of operations remains relatively unexplored. As
these manipulations require joint-level control to regulate the complete poses
of the robots, we develop AirExo, a low-cost, adaptable, and portable dual-arm
exoskeleton, for teleoperation and demonstration collection. As collecting
teleoperated data is expensive and time-consuming, we further leverage AirExo
to collect cheap in-the-wild demonstrations at scale. Under our in-the-wild
learning framework, we show that with only 3 minutes of the teleoperated
demonstrations, augmented by diverse and extensive in-the-wild data collected
by AirExo, robots can learn a policy that is comparable to or even better than
one learned from teleoperated demonstrations lasting over 20 minutes.
Experiments demonstrate that our approach enables the model to learn a more
general and robust policy across the various stages of the task, enhancing the
success rates in task completion even with the presence of disturbances.
Project website: https://airexo.github.io/
