混合强化学习让机械臂零样本完成真实果园剪枝
Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning
机器人纯仿真训练就能剪苹果枝和樱桃枝，光流代替3D重建，38次真实试验零样本跑通，还赢过RRT-Connect基线。
论文提出端到端流水线，用混合强化学习结合离线示范与在线仿真，训练闭环视觉运动控制器，全程只在仿真中训练后零样本部署到真实果园。
控制器仅依赖腕部相机的光流输入，绕开了完整3D重建。在3000个剪枝点的仿真评估中，V-Trellis苹果成功率49.
9%，UFO樱桃46.0%。
38次实体试验（28次户外果园、10次室内）验证零样本sim-to-real迁移，实验室中该策略胜过RRT-Connect基线。
Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning
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.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Key entities and technical topics under continuous tracking include 强化学习, 具身智能, sim-to-real, 农业机器人.
Source attribution: curated from arXiv cs.AI (@Abhinav Jain, Cindy Grimm, Stefan Lee).
