Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors
For robotic grasping, this method enables faster stability prediction without requiring physical contact, addressing a bottleneck in real-time grasp failure detection.
The paper proposes a contact-free grasp stability predictor using multi-zone time-of-flight sensors, achieving 86% accuracy on unseen objects while operating at 15 Hz, significantly faster than tactile-based methods.
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.