Intelligent Escape

Intelligent escape research focuses on developing algorithms and systems that enable agents, whether robots or software, to effectively and efficiently navigate away from undesirable states or situations, such as threats or local optima. Current research explores diverse approaches, including bio-inspired methods (e.g., fish schooling behavior), optimization algorithms (like quantum-inspired metaheuristics and differential dynamic programming), and reinforcement learning techniques with safety constraints. This field is significant for improving the robustness and reliability of autonomous systems across various applications, from robotics and search-and-rescue to software optimization and machine learning.