  Audio signals provide rich information for the robot interaction and object
properties through contact. These information can surprisingly ease the
learning of contact-rich robot manipulation skills, especially when the visual
information alone is ambiguous or incomplete. However, the usage of audio data
in robot manipulation has been constrained to teleoperated demonstrations
collected by either attaching a microphone to the robot or object, which
significantly limits its usage in robot learning pipelines. In this work, we
introduce ManiWAV: an 'ear-in-hand' data collection device to collect
in-the-wild human demonstrations with synchronous audio and visual feedback,
and a corresponding policy interface to learn robot manipulation policy
directly from the demonstrations. We demonstrate the capabilities of our system
through four contact-rich manipulation tasks that require either passively
sensing the contact events and modes, or actively sensing the object surface
materials and states. In addition, we show that our system can generalize to
unseen in-the-wild environments, by learning from diverse in-the-wild human
demonstrations. Project website: https://mani-wav.github.io/
