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

TL;DR

This paper introduces a contact-free grasp stability prediction method using in-hand time-of-flight sensors, enabling rapid classification without grasping, with high accuracy demonstrated on real-world objects.

Contribution

The novel contact-free stability predictor leverages multi-zone sensors and achieves real-time classification, improving speed over contact-based methods.

Findings

Achieved 86.0% accuracy on test objects.

Collected over 2,500 real-world grasps for training.

Operates at 15 Hz for rapid stability assessment.

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.