Hybrid Planning

Hybrid planning integrates diverse planning approaches, such as classical methods and reinforcement learning, to leverage the strengths of each for improved efficiency and robustness in complex tasks. Current research focuses on combining symbolic reasoning with continuous control, integrating learned models with physics-based or geometric planners, and developing hybrid architectures that adapt to unforeseen circumstances or novel environments. This interdisciplinary field is advancing the capabilities of robots and autonomous systems in challenging real-world scenarios, impacting areas like robotics, autonomous driving, and pandemic response planning.