用视觉流控制机械臂，实现果园树枝的自动修剪。
Learning to Prune Branches in Modern Tree-Fruit Orchards
- 仅用腕装相机的光流图训练闭环视觉运动控制器。
- 零样本迁移在真实果园中达30%成功率，为理想规划器的一半。
- 无需3D重建，适合自动化农业场景落地。
休眠期果树修剪劳动密集但对现代高产果园至关重要。本文提出一种闭环视觉运动控制器，用于机器人修剪。该控制器引导切割工具穿越杂乱树体环境，精准到达目标切割点，并确保刀具与枝条垂直。通过一个新型果园仿真环境训练控制器，该仿真捕捉了目标苹果园配置下的枝条几何分布。与传统需完整3D重建的方法不同，本控制器仅依赖腕部摄像头的光流图像。我们在仿真和真实世界中部署所学策略，针对示例的V-Trellis无花果树实现零样本迁移，成功率达30%，约为理想规划器性能的一半。
原文摘要 · Abstract (English)
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.
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