用视觉预测抓取力，避免枣子被捏坏
V2F: Vision-Informed Grasp Force Prediction for Damage-Aware Robotic Handling of Date Fruits
- 通过视觉识别枣子形状，结合物理模型预测安全抓力
- 模型在未见品种上预测准确率达R²≈0.7，变形小于1mm
- 适合需要轻柔操作的生鲜水果机器人分拣
本文提出一种面向枣子机器手操作的视觉引导抓取力预测框架。针对高脱离力与低压痕阈值的双重挑战，首先对枣子样本进行力学表征，定义安全抓握范围并量化果实几何与生物屈服应力的关系。构建了视觉到力（V2F）流程：结合计算机视觉分割、主动轮廓优化与几何特征提取，接入物理启发的残差神经网络以增强赫兹接触方程。模型将非接触式视觉描述符与品种元数据映射为安全抓取力，跨未见品种组验证的均方决定系数达R²≈0.7，已属生物组织固有变异性下的良好表现。实验使用夹爪与负载单元验证，预测力可实现多种枣子稳定操作，残余变形低于1 mm，无可见损伤。结果表明，预先的视觉驱动力估计可替代缓慢且可能造成损伤的触觉探索，实现更安全的易损水果机器人处理。
原文摘要 · Abstract (English)
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.
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