@inproceedings{discovery10178561,
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {Ha Long, Vietnam},
       publisher = {IEEE},
       booktitle = {Proceedings of the 2024 IEEE/SICE International Symposium on System Integration (SII)},
           month = {January},
            year = {2024},
           title = {Reinforcement Learning-based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language-Image Learning},
        keywords = {Location awareness,
Affordances,
Pipelines,
Grasping,
System integration,
Robots,
Videos},
        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.},
             url = {https://doi.org/10.1109/SII58957.2024.10417178},
          author = {Long, Xiang and Beddow, Luke and Hadjivelichkov, Denis and Delfaki, Andromachi Maria and Wurdemann, Helge and Kanoulas, Dimitrios}
}