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

• URL: http://arxiv.org/abs/2605.05461v1
• Date: Wed, 06 May 2026 21:37:31 GMT
• Title: Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors
• Abstract summary: We propose a contact-free grasp stability predictor using multi-zone time-of-flight sensors mounted in the distal links of a gripper.<n>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.
• Score: 4.511923587827302
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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