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arXiv · 2605.05461

Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors

Kyle DuFrene·Cindy Grimm
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Abstract

Current approaches to grasp planning for robotics demonstrate high success rates, but degrade with noisy sensors and other factors. Previous works have proposed tactile-based grasp stability classifiers to detect failures, but these approaches rely on making contact and grasping the object to do so. We propose a contact-free grasp stability predictor using multi-zone time-of-flight sensors mounted in the distal links of a gripper. Our method, as it does not require grasping the object to make a prediction, significantly speeds up the stability classification process, cycling at 15 Hz. We collected over 2,500 real-world grasps across 15 objects to train a classifier. Additionally, we conducted grasp attempts over six additional unseen objects, three for validation and model selection, and three for model testing. Our approach demonstrated strong classification performance, with an accuracy of 85.5% on validation and 86.0% on test objects.

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Kyle DuFrene, Cindy Grimm. 2026-05-06. Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors. https://arxiv.org/abs/2605.05461

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