Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors

• URL: http://arxiv.org/abs/2403.14526v1
• Date: Thu, 21 Mar 2024 16:26:19 GMT
• Title: Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors
• Authors: Nikolaos Tsagkas, Jack Rome, Subramanian Ramamoorthy, Oisin Mac Aodha, Chris Xiaoxuan Lu,
• Abstract summary: Our work explores the grounding of fine-grained part descriptors for precise manipulation in a zero-shot setting. We tackle the problem by framing it as a dense semantic part correspondence task. Our model returns a gripper pose for manipulating a specific part, using as reference a user-defined click from a source image of a visually different instance of the same object.
• Score: 30.579707929061026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precise manipulation that is generalizable across scenes and objects remains a persistent challenge in robotics. Current approaches for this task heavily depend on having a significant number of training instances to handle objects with pronounced visual and/or geometric part ambiguities. Our work explores the grounding of fine-grained part descriptors for precise manipulation in a zero-shot setting by utilizing web-trained text-to-image diffusion-based generative models. We tackle the problem by framing it as a dense semantic part correspondence task. Our model returns a gripper pose for manipulating a specific part, using as reference a user-defined click from a source image of a visually different instance of the same object. We require no manual grasping demonstrations as we leverage the intrinsic object geometry and features. Practical experiments in a real-world tabletop scenario validate the efficacy of our approach, demonstrating its potential for advancing semantic-aware robotics manipulation. Web page: https://tsagkas.github.io/click2grasp

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