Belief Space Planning

Belief space planning (BSP) addresses the challenge of robotic decision-making under uncertainty by explicitly planning in the space of possible robot beliefs about the environment. Current research focuses on improving the efficiency and scalability of BSP algorithms, particularly for high-dimensional state spaces and multi-robot systems, often employing techniques like hierarchical planning, informed search methods (e.g., belief trees), and learned approximations to handle complex belief representations. These advancements are crucial for enabling robust and safe autonomy in real-world applications, such as autonomous driving, exploration, and manipulation in uncertain environments.