TY - GEN A1 - Vecerik, Mel A1 - Doersch, Carl A1 - Yang, Yi A1 - Davchev, Todor A1 - Aytar, Yusuf A1 - Zhou, Guangyao A1 - Hadsell, Raia A1 - Agapito, Lourdes A1 - Scholz, Jon PB - IEEE SP - 5397 N2 - 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. UR - https://doi.org/10.1109/ICRA57147.2024.10611409 ID - discovery10196975 T3 - IEEE International Conference on Robotics and Automation (ICRA) Y1 - 2024/08/08/ KW - Training KW - Visualization KW - Tracking KW - Stacking KW - Production facilities KW - Planning KW - Task analysis N1 - This version is the author-accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions. EP - 5403 SN - 1050-4729 TI - RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation CY - Yokohama, Japan AV - public ER -