从多尺度模仿中学习人行道自动驾驶：带有纠正行为扩展
Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion
AI总结：
本文提出通过纠正行为扩展和多尺度模仿学习提升人行道微出行的鲁棒性和泛化能力，通过数据和模型双重改进解决传统模仿学习的局限。
摘要：
人行道微出行是解决最后一公里交通的有前景的解决方案，但当前基于学习的控制方法在复杂城市环境中表现不佳。模仿学习（IL）通过人类示范学习策略，但其依赖固定的离线数据常导致累积误差、鲁棒性差和泛化能力弱。为解决这些问题，我们提出一个框架，通过纠正行为扩展和多尺度模仿学习推进IL。在数据方面，我们通过添加多样化的纠正行为和传感器增强来扩充遥控数据集，使策略能够学习从自身错误中恢复。在模型方面，我们引入了多尺度IL架构，通过基于时间跨度的轨迹聚类和分层监督，捕捉短时间交互行为和长时间目标导向意图。现实世界实验表明，我们的方法在多样化的 sidewalk 场景中显著提高了鲁棒性和泛化能力。
英文摘要：
Sidewalk micromobility is a promising solution for last-mile transportation, but current learning-based control methods struggle in complex urban environments. Imitation learning (IL) learns policies from human demonstrations, yet its reliance on fixed offline data often leads to compounding errors, limited robustness, and poor generalization. To address these challenges, we propose a framework that advances IL through corrective behavior expansion and multi-scale imitation learning. On the data side, we augment teleoperation datasets with diverse corrective behaviors and sensor augmentations to enable the policy to learn to recover from its own mistakes. On the model side, we introduce a multi-scale IL architecture that captures both short-horizon interactive behaviors and long-horizon goal-directed intentions via horizon-based trajectory clustering and hierarchical supervision. Real-world experiments show that our approach significantly improves robustness and generalization in diverse sidewalk scenarios.
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。
