Grasp Transfer for Deformable Objects by Functional Map Correspondence

TL;DR

This paper introduces a novel, learning-free method using functional map correspondence to adapt robotic grasps on deformable objects, improving grasp stability and accuracy without prior training.

Contribution

It presents a new FM-based approach for deformable object grasping that does not rely on learning, enabling effective adaptation of grasp hypotheses to deformed shapes.

Findings

Outperforms two state-of-the-art correspondence methods in grasp stability

Achieves higher accuracy in grasping deformed objects

Effective across multiple objects and deformation levels

Abstract

Handling object deformations for robotic grasping is still a major problem to solve. In this paper, we propose an efficient learning-free solution for this problem where generated grasp hypotheses of a region of an object are adapted to its deformed configurations. To this end, we investigate the applicability of functional map (FM) correspondence, where the shape matching problem is treated as searching for correspondences between geometric functions in a reduced basis. For a user selected region of an object, a ranked list of grasp candidates is generated with local contact moment (LoCoMo) based grasp planner. The proposed FM-based methodology maps these candidates to an instance of the object that has suffered arbitrary level of deformation. The best grasp, by analysing its kinematic feasibility while respecting the original finger configuration as much as possible, is then executed on the object. We have compared the performance of our method with two different state-of-the-art correspondence mapping techniques in terms of grasp stability and region grasping accuracy for 4 different objects with 5 different deformations.