AgriGen: Large-Scale Scene Generation Framework for Photorealistic Agricultural Robotics Simulation

2026-09-22

AI Summary

为解决农业机器人研究中数据获取困难的问题，本文提出了一个基于Isaac Sim的大规模农业场景生成框架AgriGen，支持高真实感渲染和物理模拟。

Abstract

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/

Problem

• Agricultural Robotics
• Field Access
• Geographic Variability
• Seasonal Cycles

Innovation

• Large-Scale Scene Generation
• Photorealistic Rendering
• Agricultural Robotics Simulation
• Domain Randomization