发表机构
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对枣果处理难题，开发V2F框架，结合计算机视觉与物理信息残差神经网络，将视觉描述符和品种元数据映射来预测抓握力，平均验证性能\(R^2 \approx 0.7\)，实验表明可稳定操作枣果，实现更安全的机器人处理。
AI 中文摘要
本文提出了一种用于枣果机器人处理的视觉辅助抓握力预测框架。针对枣果高脱离力和低损伤阈值的双重挑战，首先对枣果样本进行力学表征，以定义安全抓握包络并量化果实几何形状与生物屈服应力之间的关系。开发了一个视觉到力（V2F）管道，将基于计算机视觉的分割、主动轮廓细化和几何特征提取与增强赫兹接触方程的物理信息残差神经网络相结合。所得模型将非接触视觉描述符和品种元数据映射以预测安全抓握力，在未见品种组上平均验证性能为\(R^2 \approx 0.7\)。实验验证表明预测力能实现对不同类型枣果的稳定操作，残余变形低于1毫米且无明显损伤。结果表明，先发制人的视觉驱动力估计可取代缓慢且可能造成损伤的触觉探索，实现对易碎果实更安全的机器人处理。
英文摘要
This paper presents a vision-informed grasp force prediction framework for robotic handling of date fruits. Addressing the dual challenge of high detachment forces and low bruise thresholds, we first conduct mechanical characterization on date samples to define a safe grasping envelope and quantify the relationship between fruit geometry and bioyield stress. In this work, we develop a Vision-to-Force (V2F) pipeline that combines computer vision-based segmentation, active-contour refinement, and geometric feature extraction with a physics-informed residual neural network that augments a Hertz contact equation. The resulting model maps non-contact visual descriptors and cultivar metadata to predict a safe grasp force with mean validation performance of $R^2 \approx 0.7$ across unseen cultivar groups, which is a good result given the inherent mechanical variability of biological tissue. Experimental validation using a gripper and load cell indicates that the predicted forces enable stable manipulation of different types of date fruits, with residual deformations below 1 mm and no observable damage. These results show that pre-emptive, vision-driven force estimation% can replace slow and potentially damaging tactile exploration , enabling safer robotic handling of fragile fruits.
CommentsAccepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026
