{"ID":2847114,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01083","arxiv_id":"2511.01083","title":"Deployable Vision-driven UAV River Navigation via Human-in-the-loop Preference Alignment","abstract":"Rivers are critical corridors for environmental monitoring and disaster response, where Unmanned Aerial Vehicles (UAVs) guided by vision-driven policies can provide fast, low-cost coverage. However, deployment exposes simulation-trained policies with distribution shift and safety risks and requires efficient adaptation from limited human interventions. We study human-in-the-loop (HITL) learning with a conservative overseer who vetoes unsafe or inefficient actions and provides statewise preferences by comparing the agent's proposal with a corrective override. We introduce Statewise Hybrid Preference Alignment for Robotics (SPAR-H), which fuses direct preference optimization on policy logits with a reward-based pathway that trains an immediate-reward estimator from the same preferences and updates the policy using a trust-region surrogate. With five HITL rollouts collected from a fixed novice policy, SPAR-H achieves the highest final episodic reward and the lowest variance across initial conditions among tested methods. The learned reward model aligns with human-preferred actions and elevates nearby non-intervened choices, supporting stable propagation of improvements. We benchmark SPAR-H against imitation learning (IL), direct preference variants, and evaluative reinforcement learning (RL) in the HITL setting, and demonstrate real-world feasibility of continual preference alignment for UAV river following. Overall, dual statewise preferences empirically provide a practical route to data-efficient online adaptation in riverine navigation.","short_abstract":"Rivers are critical corridors for environmental monitoring and disaster response, where Unmanned Aerial Vehicles (UAVs) guided by vision-driven policies can provide fast, low-cost coverage. However, deployment exposes simulation-trained policies with distribution shift and safety risks and requires efficient adaptation...","url_abs":"https://arxiv.org/abs/2511.01083","url_pdf":"https://arxiv.org/pdf/2511.01083v1","authors":"[\"Zihan Wang\",\"Jianwen Li\",\"Li-Fan Wu\",\"Nina Mahmoudian\"]","published":"2025-11-02T21:30:29Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}