  For robots to be useful outside labs and specialized factories we need a way
to teach them new useful behaviors quickly. Current approaches lack either the
generality to onboard new tasks without task-specific engineering, or else lack
the data-efficiency to do so in an amount of time that enables practical use.
In this work we explore dense tracking as a representational vehicle to allow
faster and more general learning from demonstration. Our approach utilizes
Track-Any-Point (TAP) models to isolate the relevant motion in a demonstration,
and parameterize a low-level controller to reproduce this motion across changes
in the scene configuration. We show this results in robust robot policies that
can solve complex object-arrangement tasks such as shape-matching, stacking,
and even full path-following tasks such as applying glue and sticking objects
together, all from demonstrations that can be collected in minutes.
