AgriGen：面向逼真农业机器人仿真的大规模场景生成框架
AgriGen: Large-Scale Scene Generation Framework for Photorealistic Agricultural Robotics Simulation
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中文总结 AI 辅助
提出AgriGen框架，基于Isaac Sim和ROS，程序化生成大规模逼真农业环境，支持行栽作物、果园和葡萄园，以解决农业机器人数据获取难、评估受限的问题。
中文摘要 AI 辅助
农业机器人技术正在快速发展，但其进展仍受限于有限的田间访问机会、对田间条件缺乏控制、地理差异以及季节性作物周期。这些因素使得获取多样化的农业数据集变得困难且成本高昂，导致评估受限和系统鲁棒性降低。虽然其他机器人领域已通过高保真仿真实现了学习与评估的规模化，但农业机器人领域仍缺乏同等能力的工具。在本文中，我们提出了一个基于Isaac Sim构建的、集成ROS的框架，用于大规模程序化生成农业环境。该框架支持与机器人研究相关规模下的逼真渲染、物理仿真和域随机化，并内置对行栽作物、果园和葡萄园的支持，且可轻松扩展至其他作物类别。项目页面：此https URL
英文摘要
Agricultural robotics is advancing rapidly, yet progress remains constrained by limited field access, lack of control over field conditions, geographic variability, and seasonal crop cycles. These factors make it difficult and costly to acquire diverse agricultural datasets, resulting in limited evaluation and reduced system robustness. While other robotics domains have scaled learning and evaluation through high-fidelity simulation, agricultural robotics still lacks comparably capable tools. In this paper, we present a ROS-integrated framework, built on Isaac Sim, for large-scale procedural generation of agricultural environments. The framework supports photorealistic rendering, physics simulation, and domain randomization at scales relevant to robotics research, with built-in support for row crops, orchards, and vineyards and straightforward extensibility to additional crop categories. Project Page: https://baj31415.github.io/agrigen/
发表机构
- Georgia Tech-CNRS IRL2958（佐治亚理工学院-CNRS IRL2958）
- Notre Dame University（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。
