Grasp Synthesis Matching From Rigid To Soft Robot Grippers Using Conditional Flow Matching

• URL: http://arxiv.org/abs/2602.17110v1
• Date: Thu, 19 Feb 2026 06:12:29 GMT
• Title: Grasp Synthesis Matching From Rigid To Soft Robot Grippers Using Conditional Flow Matching
• Abstract summary: This paper proposes a novel framework to map grasp poses from a rigid gripper model to a soft Fin-ray gripper.<n>We utilize Conditional Flow Matching (CFM), a generative model, to learn this complex transformation.<n>A U-Net autoencoder conditions the CFM model on the object's geometry from a depth image, allowing it to learn a continuous mapping from an initial Anygrasp pose to a stable Fin-ray gripper pose.
• Score: 5.89165586721424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A representation gap exists between grasp synthesis for rigid and soft grippers. Anygrasp [1] and many other grasp synthesis methods are designed for rigid parallel grippers, and adapting them to soft grippers often fails to capture their unique compliant behaviors, resulting in data-intensive and inaccurate models. To bridge this gap, this paper proposes a novel framework to map grasp poses from a rigid gripper model to a soft Fin-ray gripper. We utilize Conditional Flow Matching (CFM), a generative model, to learn this complex transformation. Our methodology includes a data collection pipeline to generate paired rigid-soft grasp poses. A U-Net autoencoder conditions the CFM model on the object's geometry from a depth image, allowing it to learn a continuous mapping from an initial Anygrasp pose to a stable Fin-ray gripper pose. We validate our approach on a 7-DOF robot, demonstrating that our CFM-generated poses achieve a higher overall success rate for seen and unseen objects (34% and 46% respectively) compared to the baseline rigid poses (6% and 25% respectively) when executed by the soft gripper. The model shows significant improvements, particularly for cylindrical (50% and 100% success for seen and unseen objects) and spherical objects (25% and 31% success for seen and unseen objects), and successfully generalizes to unseen objects. This work presents CFM as a data-efficient and effective method for transferring grasp strategies, offering a scalable methodology for other soft robotic systems.

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