  To represent motions from a mechanical point of view, this paper explores
motion embedding using the motion taxonomy. With this taxonomy, manipulations
can be described and represented as binary strings called motion codes. Motion
codes capture mechanical properties, such as contact type and trajectory, that
should be used to define suitable distance metrics between motions or loss
functions for deep learning and reinforcement learning. Motion codes can also
be used to consolidate aliases or cluster motion types that share similar
properties. Using existing data sets as a reference, we discuss how motion
codes can be created and assigned to actions that are commonly seen in
activities of daily living based on intuition as well as real data. Motion
codes are compared to vectors from pre-trained Word2Vec models, and we show
that motion codes maintain distances that closely match the reality of
manipulation.
