eprintid: 10196975
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/69/75
datestamp: 2024-09-16 10:09:44
lastmod: 2024-09-16 10:10:10
status_changed: 2024-09-16 10:09:44
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Vecerik, Mel
creators_name: Doersch, Carl
creators_name: Yang, Yi
creators_name: Davchev, Todor
creators_name: Aytar, Yusuf
creators_name: Zhou, Guangyao
creators_name: Hadsell, Raia
creators_name: Agapito, Lourdes
creators_name: Scholz, Jon
title: RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
ispublished: pub
divisions: UCL
divisions: B04
divisions: F48
keywords: Training, Visualization, Tracking, Stacking, Production facilities, Planning, Task analysis
note: This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2024-08-08
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/ICRA57147.2024.10611409
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2311663
doi: 10.1109/ICRA57147.2024.10611409
lyricists_name: De Agapito Vicente, Lourdes
lyricists_id: LDEAG40
actors_name: De Agapito Vicente, Lourdes
actors_id: LDEAG40
actors_role: owner
full_text_status: public
pres_type: paper
series: IEEE International Conference on Robotics and Automation (ICRA)
publication: Proceedings - IEEE International Conference on Robotics and Automation
volume: 2024
place_of_pub: Yokohama, Japan
pagerange: 5397-5403
event_title: 2024 IEEE International Conference on Robotics and Automation (ICRA)
event_dates: 13 May 2024 - 17 May 2024
issn: 1050-4729
book_title: Proceedings - IEEE International Conference on Robotics and Automation
citation:        Vecerik, Mel;    Doersch, Carl;    Yang, Yi;    Davchev, Todor;    Aytar, Yusuf;    Zhou, Guangyao;    Hadsell, Raia;         ... Scholz, Jon; + view all <#>        Vecerik, Mel;  Doersch, Carl;  Yang, Yi;  Davchev, Todor;  Aytar, Yusuf;  Zhou, Guangyao;  Hadsell, Raia;  Agapito, Lourdes;  Scholz, Jon;   - view fewer <#>    (2024)    RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation.                     In:  Proceedings - IEEE International Conference on Robotics and Automation.  (pp. pp. 5397-5403).  IEEE: Yokohama, Japan.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10196975/1/robotapicra24.pdf