论文/arXiv 2610.22765·12 天前

基于低成本遥操作机械臂模仿学习的果梗靶向草莓采摘

面向草莓果梗精准采摘，提出视觉运动模仿学习流水线，并在低成本遥操作机械臂与边缘平台上验证。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 数据与采集

原文链接：Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm（arXiv 2610.22765 · 发布日期：2026-10-16）

Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm

面向草莓果梗精准采摘，提出视觉运动模仿学习流水线，并在低成本遥操作机械臂与边缘平台上验证。

核心要点

• 草莓果梗直径小、与叶片颜色相近且需精细力控，传统方法难以在遮挡冠层中统一感知、规划与接触操作。
• 方法从人类遥操作示范采集数据，训练带ResNet-50视觉骨干的ACT策略，统一视觉感知、运动规划和接触操作。
• 训练策略在130次示范、100K梯度步上损失由2.84收敛至0.091，并在Jetson AGX Orin上以15 Hz自主部署。
• 作者公开了可复现流水线，包括数据集、模型权重、代码、配置和部署说明。

验证范围与局限：验证包括130次示范训练的损失收敛和Jetson AGX Orin边缘平台部署，摘要未报告采摘成功率。

英文摘要 (Abstract)

Precise pedicel-targeted grasping in strawberry harvesting remains a critical bottleneck due to the pedicel's small diameter (1.3-3 mm), color similarity with surrounding foliage, and precise force control required for clean abscission zone detachment. As a market-grade crop, strawberry aesthetic value makes bruise-free pedicel detachment essential over direct fruit grasping. Classical approaches struggle to unify vision models, motion planning, and contact manipulation into a robust framework, particularly in occluded canopy environments. Therefore, this work presents a visuomotor imitation learning pipeline for pedicel-targeted strawberry harvesting that unifies perception, motion planning, and contact manipulation from collecting human teleoperation demonstrations, training an Action Chunking with Transformers (ACT) policy with a ResNet-50 visual backbone, and deploying the learned policy autonomously on a Jetson AGX Orin edge platform at 15 Hz. The trained policy achieved consistent loss convergence from 2.84 to 0.091 over 100K gradient steps on 130 demonstrations. We have also established a reproducible open-source pipeline with publicly released dataset and model weights for the agricultural robotics community. Full dataset is available at, https://huggingface.co/datasets/nitindominicrai/strawberry_pedicel_grasp_teleoperation and trained policy at, https://huggingface.co/nitindominicrai/act_strawberry_pedicel. To replicate this work, all code, configurations, and deployment instructions are publicly available at, https://github.com/nitin-dominic/autonomous_strawberry_pedicel_harvesting.

作者：Nitin Rai, Won Suk Lee, Hongyoung Leon, Heping Zhu

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.22765：Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm访问 2026-10-21