少即是多：从有限数据实现可扩展的视觉导航
Less Is More: Scalable Visual Navigation from Limited Data
AI总结：
本文提出Less is More方法，通过结合有限专家示范与规划器生成的监督，实现高效视觉导航。
摘要：
模仿学习为移动机器人中的目标条件视觉导航提供了一个强大的框架，能够实现避障的同时尊重人类偏好和社会规范。然而，其有效性严重依赖于训练数据的质量和多样性。在本文中，我们展示了如何利用经典的几何规划器生成合成轨迹，以补充昂贵的人类示范。我们训练了一个基于Transformer的视觉导航策略Less is More（LiMo），该策略能够从单个RGB观测中预测目标条件的SE(2)轨迹，并发现通过将有限的专家示范与规划器生成的监督相结合，可以显著提升性能。通过消融分析和互补的定性和定量分析，我们阐述了数据集规模和多样性如何影响规划性能。我们展示了真实机器人部署，并论证了鲁棒的视觉导航并非仅仅通过收集更多示范，而是通过战略性地整理多样且高质量的数据集来实现。我们的结果表明，可扩展的、与具身相关的几何监督是实现数据高效视觉导航的可行途径。
英文摘要：
Imitation learning provides a powerful framework for goal-conditioned visual navigation in mobile robots, enabling obstacle avoidance while respecting human preferences and social norms. However, its effectiveness depends critically on the quality and diversity of training data. In this work, we show how classical geometric planners can be leveraged to generate synthetic trajectories that complement costly human demonstrations. We train Less is More (LiMo), a transformer-based visual navigation policy that predicts goal-conditioned SE(2) trajectories from a single RGB observation, and find that augmenting limited expert demonstrations with planner-generated supervision yields substantial performance gains. Through ablations and complementary qualitative and quantitative analyses, we characterize how dataset scale and diversity affect planning performance. We demonstrate real-robot deployment and argue that robust visual navigation is enabled not by simply collecting more demonstrations, but by strategically curating diverse, high-quality datasets. Our results suggest that scalable, embodiment-specific geometric supervision is a practical path toward data-efficient visual navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
