用非接触式传感器提前预测抓取稳定性，提升机器人抓取效率。
Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors
- 通过机械臂末端的多区飞行时间传感器实现不接触物体的抓稳预测。
- 在15个物体上训练，6个未见物体测试，准确率达85.5%~86.0%。
- 每秒15次循环，适合对速度要求高的真实场景应用。
当前机器人抓取规划方法虽成功率高，但在传感器噪声等干扰下性能下降。以往方法依赖接触检测抓取失败，而本工作提出一种基于安装在夹爪远端的多区域飞行时间（Time-of-Flight）传感器的非接触式抓取稳定性预测方法。该方法无需实际抓取即可完成预测，显著加速稳定性分类，支持15 Hz的运行频率。我们在15种物体上收集了超过2,500次真实抓取数据以训练分类器，并在另外6种未见过的物体上进行测试：3个用于验证与模型选择，3个用于最终评估。实验结果显示，该方法在验证集上达到85.5%的准确率，在测试集上达到86.0%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。