%C Ha Long, Vietnam
%X 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.
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%I IEEE
%K Location awareness,
Affordances,
Pipelines,
Grasping,
System integration,
Robots,
Videos
%L discovery10178561
%D 2024
%T Reinforcement Learning-based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language–Image Learning
%A Xiang Long
%A Luke Beddow
%A Denis Hadjivelichkov
%A Andromachi Maria Delfaki
%A Helge Wurdemann
%A Dimitrios Kanoulas
%B Proceedings of the 2024 IEEE/SICE International Symposium on System Integration (SII)