这个方法不用几何建模，直接用潜在空间规划让农业机器人在杂乱玉米地里也能走。语义故障减少2.4倍，零样本迁移到真实世界。
LeCropFollow是一种基于潜在空间规划的视觉导航框架，用于非结构化作物田地。它结合自监督语义热图提取器和TD-MPC2模型基强化学习规划器，直接在潜在流形中优化轨迹。该方法实现了从简化仿真到真实世界的零样本迁移，无需微调。在晚期玉米田的实验中，LeCropFollow在种植间隙的语义故障比基于关键点的方法减少2.4倍，匹配现有基准但在非结构化行中显著更优。
LeCropFollow: Latent Space Planning for Navigation in Unstructured Crop Fields
Unstructured navigational features, such as irregular planting or discontinuities, remain the primary failure mode for under-canopy agricultural robots. Existing geometric approaches often fail in these scenarios because they compress high-dimensional visual data into deterministic spatial references, effectively discarding the uncertainty and semantic context required to navigate ambiguous terrain. To address this, we present LeCropFollow, a visual navigation framework that bypasses explicit geometric modeling in favor of a learned latent representation. By integrating a self-supervised semantic heatmap extractor with TD-MPC2, a Model-Based Reinforcement Learning (MBRL) planner, our system optimizes trajectories directly within a latent manifold. The framework operates over the uncompressed heatmap signal, preserving the semantic context that geometric reductions discard. We demonstrate that this representational shift enables zero-shot transfer from simplified simulation to the physical world without fine-tuning. Extensive field experiments in late-stage corn fields show that LeCropFollow matches state-of-the-art baselines in unstructured rows but significantly outperforms them in plantation gaps, achieving a 2.4x reduction in semantic failures compared to keypoint-based methods. These results suggest that latent planning offers a robust alternative to geometric estimation for operations in heterogeneous agricultural environments. Code, models, and data available: https://felipe-tommaselli.github.io/lecropfollow .