  Truly intelligent agents need to capture the interplay of all their senses to
build a rich physical understanding of their world. In robotics, we have seen
tremendous progress in using visual and tactile perception; however, we have
often ignored a key sense: sound. This is primarily due to the lack of data
that captures the interplay of action and sound. In this work, we perform the
first large-scale study of the interactions between sound and robotic action.
To do this, we create the largest available sound-action-vision dataset with
15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By
tilting objects and allowing them to crash into the walls of a robotic tray, we
collect rich four-channel audio information. Using this data, we explore the
synergies between sound and action and present three key insights. First, sound
is indicative of fine-grained object class information, e.g., sound can
differentiate a metal screwdriver from a metal wrench. Second, sound also
contains information about the causal effects of an action, i.e. given the
sound produced, we can predict what action was applied to the object. Finally,
object representations derived from audio embeddings are indicative of implicit
physical properties. We demonstrate that on previously unseen objects, audio
embeddings generated through interactions can predict forward models 24% better
than passive visual embeddings. Project videos and data are at
https://dhiraj100892.github.io/swoosh/
