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

TL;DR

This paper introduces a novel framework using Conditional Flow Matching to transfer grasp poses from rigid to soft robotic grippers, significantly improving success rates and generalization to unseen objects.

Contribution

It presents a data-efficient, generative modeling approach to map rigid grasp poses to soft grippers, addressing the representation gap and enabling better transfer of grasp strategies.

Findings

Higher success rates for soft grippers on seen and unseen objects (up to 46%)

Significant improvement over baseline rigid poses (up to 40% increase)

Effective generalization to novel objects with soft grasping strategies

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.