Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion
This work addresses the challenge of reliable autonomous sidewalk navigation for last-mile transportation, representing an incremental improvement over existing imitation learning methods.
The paper tackled the problem of sidewalk micromobility control in complex urban environments by proposing a framework that enhances imitation learning with corrective behavior expansion and multi-scale imitation, resulting in significantly improved robustness and generalization in real-world experiments.
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.