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For robots to be useful outside labs and specialized factories we need a wayto teach them new useful behaviors quickly. Current approaches lack either thegenerality to onboard new tasks without task-specific engineering, or else lackthe 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 allowfaster and more general learning from demonstration. Our approach utilizesTrack-Any-Point (TAP) models to isolate the relevant motion in a demonstration,and parameterize a low-level controller to reproduce this motion across changesin the scene configuration. We show this results in robust robot policies thatcan solve complex object-arrangement tasks such as shape-matching, stacking,and even full path-following tasks such as applying glue and sticking objectstogether, all from demonstrations that can be collected in minutes.

RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation