From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation
arXiv:2606.12603v1 Announce Type: cross 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 lac
The increasing demand for practical autonomous micro-mobility solutions and the limitations of current imitation learning approaches for complex, long-horizon tasks are driving innovation in reliable sidewalk navigation techniques.
Improving autonomous sidewalk navigation is critical for expanding micro-mobility applications, impacting sectors from logistics to personal assistance, and represents a step towards more robust general-purpose AI agents.
This paper proposes a method to overcome compounding errors in imitation learning for sidewalk navigation by integrating human preferences, leading to more reliable and safer autonomous systems for complex, real-world environments.
- · Robotic delivery companies
- · Assistive technology developers
- · Urban logistics providers
- · AI research in robotics
- · Companies relying on less robust navigation solutions
- · Traditional human-operated micro-mobility services
More widespread deployment of autonomous robots for last-mile delivery and personal mobility in urban environments.
Increased demand for robust, explainable AI systems that can safely interact with complex, unpredictable human environments.
Acceleration of research and development in lightweight, reliable perception stacks for resource-constrained robotic platforms, democratizing access to advanced autonomous capabilities.
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Read at arXiv cs.AI