Offline Planning

Offline planning focuses on pre-computing optimal paths or strategies for robots and other autonomous systems before execution, aiming to maximize efficiency and robustness. Current research emphasizes developing efficient algorithms (like branch and bound, genetic algorithms, and A*), incorporating learned models (e.g., diffusion models, neural ODEs) for improved prediction and decision-making, and designing resilient plans that adapt to unexpected events or robot failures. These advancements are crucial for improving the reliability and performance of autonomous systems in diverse applications, from industrial robotics and agriculture to multi-agent coordination and search-and-rescue operations.