aiqbench analysis
This research presents a visuotactile framework for robots to estimate object material properties in real time, enabling them to apply the precise minimum force needed to grasp delicate items without damage. By combining visual and tactile data with simulations validated on physical hardware, the study advances autonomous manipulation capabilities through adaptive control strategies that account for elasticity and friction.
How Firm Should a Grasp Be?
arXiv:2610.11221v1 Announce Type: new Abstract: An ideal robot grasp is firm enough to securely handle an object, yet gentle enough to avoid damaging it. Achieving this balance requires knowledge of the object's material properties, such as its mass, elasticity, and surface friction. These properties, however, are seldom precisely known a priori. In this work, we propose a visuotactile approach to estimating material properties in real time, during the process of grasping. Our method uses these estimated properties to determine the minimum grasp force required to handle the object. We contribute a new dataset of real-world objects (fruits and vegetables) with measured physical properties (shape, mass, elasticity, and friction), which we use to construct our force estimation model via simulations. We experimentally validate our approach to grasp force control using a robot with a parallel-jaw gripper. We demonstrate our system's ability to gently grasp a wide variety of objects, in each case adapting to their unique physical properties.
Title:How Firm Should a Grasp Be?
Abstract:An ideal robot grasp is firm enough to securely handle an object, yet gentle enough to avoid damaging it. Achieving this balance requires knowledge of the object's material properties, such as its mass, elasticity, and surface friction. These properties, however, are seldom precisely known a priori. In this work, we propose a visuotactile approach to estimating material properties in real time, during the process of grasping. Our method uses these estimated properties to determine the minimum grasp force required to handle the object. We contribute a new dataset of real-world objects (fruits and vegetables) with measured physical properties (shape, mass, elasticity, and friction), which we use to construct our force estimation model via simulations. We experimentally validate our approach to grasp force control using a robot with a parallel-jaw gripper. We demonstrate our system's ability to gently grasp a wide variety of objects, in each case adapting to their unique physical properties.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2610.11221 [cs.RO]
(or arXiv:2610.11221v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.11221 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 257 words
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