eprintid: 10178561
rev_number: 13
eprint_status: archive
userid: 699
dir: disk0/10/17/85/61
datestamp: 2023-10-11 10:13:04
lastmod: 2024-03-08 14:01:14
status_changed: 2024-03-08 14:01:14
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Long, Xiang
creators_name: Beddow, Luke
creators_name: Hadjivelichkov, Denis
creators_name: Delfaki, Andromachi Maria
creators_name: Wurdemann, Helge
creators_name: Kanoulas, Dimitrios
title: Reinforcement Learning-based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language–Image Learning
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Location awareness,
Affordances,
Pipelines,
Grasping,
System integration,
Robots,
Videos
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: We present a novel robotic grasping system using
a caging-style gripper, that combines one-shot affordance localization and zero-shot object identification. We demonstrate an
integrated system requiring minimal prior knowledge, focusing
on flexible few-shot object agnostic approaches. For grasping
a novel target object, we use as input the color and depth
of the scene, an image of an object affordance similar to the
target object, and an up to three-word text prompt describing
the target object. We demonstrate the system using real-world
grasping of objects from the YCB benchmark set, with four
distractor objects cluttering the scene. Overall, our pipeline
has a success rate of the affordance localization of 96%, object
identification of 62.5%, and grasping of 72%. Videos are on
the project website: https://sites.google.com/view/
rl-affcorrs-grasp.
date: 2024-01-08
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/SII58957.2024.10417178
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2095337
doi: 10.1109/SII58957.2024.10417178
lyricists_name: Kanoulas, Dimitrios
lyricists_id: DKANO15
actors_name: Kanoulas, Dimitrios
actors_id: DKANO15
actors_role: owner
full_text_status: public
pres_type: paper
place_of_pub: Ha Long, Vietnam
event_title: The 2024 16th IEEE/SICE International Symposium on System Integration
event_location: Ha Long, Vietnam
event_dates: 8 Jan 2024 - 11 Jan 2024
book_title: Proceedings of the 2024 IEEE/SICE International Symposium on System Integration (SII)
citation:        Long, Xiang;    Beddow, Luke;    Hadjivelichkov, Denis;    Delfaki, Andromachi Maria;    Wurdemann, Helge;    Kanoulas, Dimitrios;      (2024)    Reinforcement Learning-based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language–Image Learning.                     In:  Proceedings of the 2024 IEEE/SICE International Symposium on System Integration (SII).    IEEE: Ha Long, Vietnam.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10178561/1/sii_2024_xiang.pdf