From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation
Autonomous long-horizon sidewalk navigation is essential for micro-mobility applications such as robotic food delivery and assistive electronic wheelchairs. Unlike autonomous driving on the road, long-horizon sidewalk navigation requires precise maneuvering through unpredictable sidewalk terrains and pedestrians, with a lightweight perception stack as minimal as a single monocular RGB camera. While imitation learning (IL) from demonstrations offers a practical solution, the resulting autopilot policy often suffers from compounding errors, a lack of social compliance on sidewalks, and deficiencies in counterfactual reasoning to handle complex situations. To address these challenges, we int...
| Authors | Honglin He, Zhizheng Liu, Yukai Ma et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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