RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation

• URL: http://arxiv.org/abs/2308.15975v2
• Date: Thu, 31 Aug 2023 15:29:44 GMT
• Title: RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
• Authors: Mel Vecerik and Carl Doersch and Yi Yang and Todor Davchev and Yusuf Aytar and Guangyao Zhou and Raia Hadsell and Lourdes Agapito and Jon Scholz
• Abstract summary: Track-Any-Point (TAP) models 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.
• Score: 36.43143326197769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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