Abstract
Dormant tree pruning is labor-intensive but essential to maintaining modern highly-productive fruit orchards. In this work we present a closed-loop visuomotor controller for robotic pruning. The controller guides the cutter through a cluttered tree environment to reach a specified cut point and ensures the cutters are perpendicular to the branch. We train the controller using a novel orchard simulation that captures the geometric distribution of branches in a target apple orchard configuration. Unlike traditional methods requiring full 3D reconstruction, our controller uses just optical flow images from a wrist-mounted camera. We deploy our learned policy in simulation and the real-world for an example V-Trellis envy tree with zero-shot transfer, achieving a 30% success rate -- approximately half the performance of an oracle planner.
Abstract (translated)
休眠树木修剪是一项劳动密集型工作，但对维持现代高产果园至关重要。在这项工作中，我们提出了一种用于机器人修剪的闭环视觉运动控制器。该控制器引导切割工具穿过茂密的树丛环境以到达指定的切割点，并确保切割面与树枝垂直。我们使用一种新颖的果园模拟器进行控制器训练，这种模拟器捕捉到了目标苹果园配置中枝条的几何分布情况。与传统方法需要完整的3D重建不同，我们的控制器仅使用来自手腕安装摄像头提供的光学流图像。我们将学到的策略部署在仿真和实际环境中，用于处理一个示例V型支架艳丽树（envy tree），实现了零样本迁移学习，在测试中达到了约30%的成功率——这大约是理想规划器（oracle planner）性能的一半。
URL
https://arxiv.org/abs/2507.23015
