Terrain awareness, i.e., the ability to identify and distinguish different
types of terrain, is a critical ability that robots must have to succeed at
autonomous off-road navigation. Current approaches that provide robots with
this awareness either rely on labeled data which is expensive to collect,
engineered features and cost functions that may not generalize, or expert human
demonstrations which may not be available. Towards endowing robots with terrain
awareness without these limitations, we introduce Self-supervised TErrain
Representation LearnING (STERLING), a novel approach for learning terrain
representations that relies solely on easy-to-collect, unconstrained (e.g.,
non-expert), and unlabelled robot experience, with no additional constraints on
data collection. STERLING employs a novel multi-modal self-supervision
objective through non-contrastive representation learning to learn relevant
terrain representations for terrain-aware navigation. Through physical robot
experiments in off-road environments, we evaluate STERLING features on the task
of preference-aligned visual navigation and find that STERLING features perform
on par with fully supervised approaches and outperform other state-of-the-art
methods with respect to preference alignment. Additionally, we perform a
large-scale experiment of autonomously hiking a 3-mile long trail which
STERLING completes successfully with only two manual interventions,
demonstrating its robustness to real-world off-road conditions.

自我监督的地形表示学习（STERLING）是一种无需额外约束数据收集的机器人地形感知方法，通过非对比表示学习实现多模态自我监督目标，为机器人导航提供相关的地形表示，该方法在机器人在野外环境的视觉导航任务中与全监督方法相当，优于其他先进方法并表现出对真实世界坎坷条件的鲁棒性。