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.

我们探讨了密集跟踪作为一种表征工具，使机器人能够更快、更通用地从示教中学习，并展示了通过密集跟踪生成的稳健机器人策略能够解决复杂的物体排列任务，如形状匹配、堆叠，甚至全路径跟踪任务，如涂胶并粘合物体，这些示教仅需数分钟即可收集。