发表机构
Stanford University School of Medicine; Jet Propulsion Laboratory; California Institute of Technology（斯坦福大学医学院; 喷气推进实验室; 加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出名为MANTLE的多任务自适应网络，基于模块化上行原理实现行星感知，在火星地形分类和巨石分割任务上取得良好性能，支持任务生命周期内新增感知能力。
AI 中文摘要
行星表面探测任务日益依赖能解释复杂地形的自主机器人平台，以确保安全导航、实现定向科学探测并提升操作效率，从维京号到毅力号的历次火星任务已证明这一点。关键感知能力中，地形分类为着陆点选择和科学分析提供上下文信息，而巨石分割则支持危险评估与路径规划。本文提出MANTLE，一种用于地形与地貌提取的多任务自适应网络。该模型采用共享的DINOv2主干网络进行高层特征提取，搭配任务特定头：用于大规模地貌分类的分类头，以及用于像素级巨石定位的分割头，二者分别在由HiRISE轨道图像和MSL表面图像构建的整理数据集上训练。分类头在7类火星地形上达到92.56%的测试准确率，分割头达到0.753的验证交并比（IoU），并在未见过的漫游车轨迹的保留测试集上表现出强跨日泛化能力。MANTLE的核心优势在于其模块化、可扩展的设计，在此正式表述为模块化上行原理：仅需共享的冻结主干网络留在星载，后续感知能力作为轻量任务特定头在地球训练后上行，无需重新训练整个模型。本工作展示了地形分类和巨石分割这两种高影响力能力，作为旨在在任务生命周期内支持更多此类能力的框架的初始实现。有了这一基础，未来的探测器无需完全成型后抵达火星，而是可通过每次上行持续学习、适应并提升能力。
英文摘要
Planetary surface exploration missions rely increasingly on autonomous robotic platforms capable of interpreting complex terrain to ensure safe navigation, enable targeted science, and improve operational efficiency, as demonstrated across past Mars missions from Viking through Perseverance. Among the key perception capabilities, landform classification provides contextual information for landing site selection and scientific analysis, while boulder segmentation supports hazard assessment and path planning. This paper presents MANTLE, a multi-task adaptive network for terrain and landform extraction. The model uses a shared DINOv2 backbone for high-level feature extraction with task-specific heads: a classification head for large-scale landform classification, and a segmentation head for pixel-wise boulder localization, each trained on curated datasets built respectively from HiRISE orbital imagery and MSL surface-level imagery. The classification head achieved a test accuracy of 92.56% across seven Martian terrain classes, while the segmentation head achieved a validation IoU of 0.753 and showed strong cross-sol generalization on a held-out test set from previously unseen rover traverses. A key advantage of MANTLE is its modular, extensible design, formalized here as the Modular Uplink Principle: only a shared, frozen backbone needs to remain onboard, while subsequent perception capabilities are trained on Earth as lightweight task-specific heads and uplinked without retraining the full model. This work demonstrates two such high-impact capabilities, terrain classification and boulder segmentation, as an initial realization of a framework built to support many more over a mission's lifetime. With this foundation, future explorers need not arrive on Mars fully formed, but can continue to learn, adapt, and grow more capable with every uplink.
Comments10 pages
