  Motions carry information about the underlying task being executed. Previous
work in human motion analysis suggests that complex motions may result from the
composition of fundamental submovements called movemes. The existence of finite
structure in motion motivates information-theoretic approaches to motion
analysis and robotic assistance. We define task embodiment as the amount of
task information encoded in an agent's motions. By decoding task-specific
information embedded in motion, we can use task embodiment to create detailed
performance assessments. We extract an alphabet of behaviors comprising a
motion without \textit{a priori} knowledge using a novel algorithm, which we
call dynamical system segmentation. For a given task, we specify an optimal
agent, and compute an alphabet of behaviors representative of the task. We
identify these behaviors in data from agent executions, and compare their
relative frequencies against that of the optimal agent using the
Kullback-Leibler divergence. We validate this approach using a dataset of human
subjects (n=53) performing a dynamic task, and under this measure find that
individuals receiving assistance better embody the task. Moreover, we find that
task embodiment is a better predictor of assistance than integrated
mean-squared-error.
