TY - GEN N2 - 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. ID - discovery10178561 UR - https://doi.org/10.1109/SII58957.2024.10417178 Y1 - 2024/01/08/ CY - Ha Long, Vietnam TI - Reinforcement Learning-based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language?Image Learning AV - public PB - IEEE A1 - Long, Xiang A1 - Beddow, Luke A1 - Hadjivelichkov, Denis A1 - Delfaki, Andromachi Maria A1 - Wurdemann, Helge A1 - Kanoulas, Dimitrios KW - Location awareness KW - Affordances KW - Pipelines KW - Grasping KW - System integration KW - Robots KW - Videos N1 - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions. ER -