# End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2509.18608 · HTML: https://mlsanity.com/paper/2509.18608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025)
- [Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models](https://mlsanity.com/paper/2308.15143/) (2023)
- [Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07119/) (2021)
- [CaRL: Learning Scalable Planning Policies with Simple Rewards](https://mlsanity.com/paper/2504.17838/) (2025)
- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)
- [Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level](https://mlsanity.com/paper/2303.01899/) (2023)
- [Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots](https://mlsanity.com/paper/2106.15029/) (2021)
- [Tracking error learning control for precise mobile robot path tracking in outdoor environment](https://mlsanity.com/paper/2103.11282/) (2021)
- [CropNav: a Framework for Autonomous Navigation in Real Farms](https://mlsanity.com/paper/2411.10974/) (2024)
- [Autonomous Navigation of 4WIS4WID Agricultural Field Mobile Robot using Deep Reinforcement Learning](https://mlsanity.com/paper/2412.18865/) (2024)

## Built on by (most cited first, 1 of 1)

- [LeCropFollow: Latent Space Planning for Navigation in Unstructured Crop Fields](https://mlsanity.com/paper/2606.31941/) (2026)

## Abstract (arXiv)

Reliable navigation in under-canopy agricultural environments remains a challenge due to GNSS unreliability, cluttered rows, and variable lighting. To address these limitations, we present an end-to-end learning-based navigation system that maps raw 3D LiDAR data directly to control commands using a deep reinforcement learning policy trained entirely in simulation. Our method includes a voxel-based downsampling strategy that reduces LiDAR input size by 95.83%, enabling efficient policy learning without relying on labeled datasets or manually designed control interfaces. The policy was validated in simulation, achieving a 100% success rate in straight-row plantations and showing a gradual decline in performance as row curvature increased, tested across varying sinusoidal frequencies and amplitudes.