Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training

2026-04-29 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

A hierarchical decision-making framework has been developed for unmanned aerial vehicle (UAV) search-and-rescue (SAR) missions, specifically designed for scenarios with limited simulation training. This framework integrates a fixed rule-based high-level advisor with an online goal-conditioned low-level reinforcement learning (RL) controller. The high-level advisor, defined offline from a structured task specification, provides interpretable guidance on recommended and avoided actions, along with regime-dependent arbitration weights. The low-level controller learns online using dense rewards and reuses experience via a mode-aware prioritized replay mechanism enhanced with rule-derived metadata. Evaluated on battery-aware multi-goal delivery and moving-target delivery in obstacle-rich environments, the method significantly improves early safety and sample efficiency by reducing collision terminations, while maintaining online adaptability to scenario-specific dynamics.

Key takeaway

For research scientists developing autonomous systems for critical missions like search-and-rescue, this framework offers a robust approach to integrate safety and efficiency. You should consider adopting a hierarchical decision-making structure that combines deterministic rule-based guidance with online reinforcement learning, especially when pre-training data or extensive simulation time is limited. This can significantly reduce collision rates and improve early mission success.

Key insights

A hybrid rule-based and RL framework enhances UAV mission safety and efficiency in limited-simulation SAR.

Principles

• Combine fixed rules with online learning.
• Prioritize safety and sample efficiency.
• Ensure adaptability to new scenarios.

Method

The method uses an offline rule-based high-level advisor for guidance and an online goal-conditioned low-level RL controller that learns from dense rewards and reuses experience with rule-derived metadata.

In practice

• Apply to battery-aware multi-goal delivery.
• Use for moving-target delivery in obstacles.

Topics

• Hierarchical Reinforcement Learning
• UAV Search-and-Rescue
• Rule-based Guidance
• Goal-Conditioned RL
• Limited Simulation Training

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.