发表机构
Oregon State University（俄勒冈州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种端到端流水线，利用混合强化学习训练视觉运动控制器，实现平面果园机器人修剪，在模拟中训练并零样本部署至真实果园，成功率约50%。
AI 中文摘要
休眠期树木修剪劳动强度大，但对于维持现代高产果园至关重要。本研究聚焦于现代平面树形训练系统的修剪，即V型棚架苹果和UFO樱桃，其中树干和主枝被训练成近似平面的墙状结构。我们提出了一种端到端流水线，用于学习机器人修剪的闭环视觉运动控制器。该控制器完全在仿真环境中使用合成生成的数据进行训练，并以零样本方式部署到真实果园中。该流水线包括平面果园树木网格的合成生成、基于物理的果园模拟器构建、通过运动规划自动收集成功修剪轨迹，以及一种新颖的混合强化学习算法进行策略学习，该算法将离线演示与在线模拟展开相结合。控制器使用腕部相机的光流输入，避免了完整三维重建的需求，并连续引导切割器穿过杂乱的分支环境，以正确的工具方向到达指定切割点。在对3000个修剪点进行的详尽模拟任务空间评估中，该策略在V型棚架苹果上达到49.9%的成功率，在UFO樱桃上达到46.0%。我们通过38次物理试验验证了所学控制器，包括在商业和实验果园中的28次室外田间试验和10次室内实验室测试，展示了零样本模拟到现实的迁移。在实验室试验中，所学策略在物理硬件上的表现也优于经典的RRT-Connect基线。
英文摘要
Dormant tree pruning is labor-intensive yet essential for maintaining modern high-productivity fruit orchards. In this work, we focus on pruning of modern planar tree training systems - V-Trellis apples and UFO cherries - where trunks and primary branches are trained into approximately planar walls. We introduce an end-to-end pipeline to learn a closed-loop visuomotor controller for robotic pruning. This controller is trained entirely using simulation and synthetically generated data and deployed in real orchards in a zero-shot manner. The pipeline comprises synthetic generation of planar orchard tree meshes, construction of a physics-based orchard simulator, automated collection of successful pruning trajectories via motion planning, and policy learning with a novel hybrid reinforcement-learning algorithm that combines offline demonstrations with online simulated rollouts. The controller uses optical-flow inputs from a wrist-mounted camera - avoiding the need for full 3D-reconstruction - and continuously guides the cutter through cluttered branch environments to a specified cutpoint with correct tool orientation. In exhaustive simulated task-space evaluations over 3,000 pruning points, the policy attains 49.9% success on V-Trellis apples and 46.0% on UFO cherries. We validate the learned controller across 38 physical trials - comprising 28 outdoor field trials in commercial and experimental orchards and 10 indoor laboratory tests - demonstrating zero-shot sim-to-real transfer. The learned policy also outperforms a classical RRT-Connect baseline on physical hardware in laboratory trials.
Commentsfor associated video file, see https://www.youtube.com/watch?v=AjlBe6A0xdo&t
