Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion
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...
| Authors | Honglin He, Yukai Ma, Brad Squicciarini et al. |
|---|---|
| Primary category | cs.RO |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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