Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of data and can be vulnerable to domain shifts. To improve generalization in novel environments, recent works have incorporated adaptation and self-supervision to develop autonomous systems that can learn from their own experiences online. However, current works often rely on significant prior data, for example minutes of human teleoperation data for each terrain type, which is difficult to scale with more environments and robots. To address these limitations, we propose SALON, a perception-action framework for fast adaptation of traversability estimates with minimal human input. SALON rapidly learns online from experience while avoiding out of distribution terrains to produce adaptive and risk-aware cost and speed maps. Within seconds of collected experience, our results demonstrate comparable navigation performance over kilometer-scale courses in diverse off-road terrain as methods trained on 100-1000x more data. We additionally show promising results on significantly different robots in different environments. Our code is available at https://theairlab.org/SALON.

翻译：在越野环境中实现自主机器人导航面临诸多挑战，这主要源于环境缺乏结构性，难以针对多样场景手工制定鲁棒的启发式规则。尽管使用人工标注或自监督数据的学习方法提升了泛化能力，但它们通常需要海量数据，且易受领域偏移的影响。为提升在新环境中的泛化性能，近期研究结合了自适应与自监督技术，开发出能够在线从自身经验中学习的自主系统。然而，现有方法往往依赖大量先验数据，例如针对每种地形类型需采集数分钟的人工遥操作数据，这难以随环境和机器人数量增加而扩展。为应对这些局限，我们提出了SALON——一种感知-行动框架，能够以最少的人工输入快速适应可通行性估计。SALON在线从经验中快速学习，同时避开分布外地形，从而生成自适应且具备风险感知的代价与速度地图。我们的实验结果表明，仅需数秒采集的经验，在多样化越野地形中千米级路径上的导航性能，即可与使用100-1000倍数据训练的方法相媲美。我们还在不同环境中的显著差异机器人上展示了有前景的结果。代码发布于 https://theairlab.org/SALON。