OrchardBench：用于农业机器人的基于物理的、GPU并行苹果园模拟基准测试
OrchardBench: A Physically-Grounded, GPU-Parallel Apple-Orchard Simulation Benchmark for Agricultural Robotics
AI总结：
研究针对农业机器人水果采摘问题，提出基于物理的、GPU并行的OrchardBench模拟基准测试，通过随机L系统生长树木，定义任务和度量套件，报告基线结果，指出分析基线有提升空间，为新自主方法提供了机会。
摘要：
机器人水果采摘是农业自动化的一个关键问题，但实地实验的成本和不可重复性限制了进展。图形学和农学中使用的树木模型几何细节丰富但物理上无活性，机器人学习中使用的GPU并行模拟器没有逼真的树木。我们提出了OrchardBench，它基于物理，在牛顿引擎上对苹果园树木进行GPU并行模拟。每棵树由随机L系统生长并实例化为完全铰接的物体，苹果是茎系上的独立物体，有可移动的树叶层。每个物理参数都有出处，通过域随机化使每个批次的世界都有不同的树，移动机械手通过几何水果感知和自主收获基线来闭环。精心设计的求解器和模型使OrchardBench能在笔记本电脑GPU上以交互速率运行多个并行环境。我们定义了任务和度量套件，并报告了基线结果。分析基线仅成功采摘了约八分之一的可及果实，为新的自主方法留出了空间。
英文摘要：
Robotic tree-fruit harvesting is a flagship problem for agricultural automation, but progress is bottlenecked by the cost and irreproducibility of field experiments: an orchard is available only weeks a year, every tree is different, and a control error can permanently damage the crop or the plant. The tree models used in graphics and agronomy are geometrically detailed but physically inert, while the GPU-parallel simulators used in robot learning contain no plausible trees. We present OrchardBench, a physically-grounded, GPU-parallel simulation of apple-orchard trees on the Newton engine. Each tree is grown by a stochastic L-system and instantiated as a fully articulated body: branches are compliant torsional spring-dampers whose stiffness follows Euler-Bernoulli beam theory, they break at a wood modulus of rupture and fall as free hinges, and apples are independent bodies on stem tethers that detach at literature-grounded pull forces and load the branch when pulled. A moving, density-controllable foliage layer occludes the canopy as real leaves do. Every physical parameter is tied to a published source. Per-environment domain randomization makes each batched world a distinct tree, and a mobile manipulator with a wrist depth camera closes the loop with geometric fruit perception and an autonomous harvesting baseline. Careful engineering of the solver and the model lets OrchardBench run many parallel environments at interactive rates on a laptop GPU. We define the tasks and a metric suite spanning harvest completeness, throughput, and plant damage (with a per-canopy-zone breakdown), and report baseline results across foliage, fruit load, terrain, canopy zone, and parallelism. The analytic baseline succeeds on about 40% of the fruit it detects and harvests only about an eighth of the reachable fruit on a tree, leaving clear headroom for novel autonomy approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
