通过纠错行为扩展提升自动驾驶在复杂环境下的鲁棒性
Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion
- 用多样化纠错行为和传感器增强数据，让模型学会自我修正
- 多尺度模仿学习框架提升对短时互动与长程目标的建模能力
- 适合研究城市人行道机器人控制与强化模仿学习的学者
人行道微出行是解决最后一公里交通的有前景方案，但现有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）虽能从人类示范中学习策略，但依赖固定离线数据常导致误差累积、鲁棒性差和泛化能力弱。为此，我们提出一种新框架，通过纠错行为扩展和多尺度模仿学习推进IL。数据层面，通过引入多样化的纠正行为和传感器增强，使策略学会从自身错误中恢复；模型层面，采用基于轨迹聚类的多尺度架构，实现对短时交互行为与长时目标导向意图的联合建模。真实场景实验表明，该方法显著提升了复杂人行道场景下的鲁棒性与泛化性能。
原文摘要 · Abstract (English)
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.
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