[2606.12603] From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation
[Submitted on 10 Jun 2026]
Title:From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation
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Abstract: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 introduce FlowPilot, a mapless navigation policy that achieves robust and efficient long-horizon navigation performance using only a monocular RGB camera. We first propose to use anchored flow matching as an action representation for policy pre-training on large-scale robot fleet data and to capture the diverse, complex, multimodal distribution of sidewalk navigation behaviors. To bridge the gap between imitation and alignment, we further design a human-in-the-loop preference learning scheme to tune the policy on a small amount of human intervention data. It strengthens the model's counterfactual reasoning and social compliance on sidewalks. We evaluate FlowPilot through extensive simulation and real-world experiments in diverse sidewalk environments. FlowPilot achieves 42% success rate and 66% route completion in simulation, while FlowPilot-HP further improves real-world robustness and social compliance, reducing IR by 40.0% and NIR by 52.1% relative to the base model.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.12603 [cs.RO]
(or arXiv:2606.12603v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.12603
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Honglin He [view email] [v1] Wed, 10 Jun 2026 19:01:31 UTC (28,214 KB)
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