Differentiable Planner

Differentiable planners are a class of algorithms that use neural networks to generate plans in a way that allows for end-to-end optimization through gradient descent. Current research focuses on improving scalability, stability, and generalization of these planners, often employing architectures like Value Iteration Networks (VINs) and Transformers, and incorporating techniques such as implicit differentiation and symmetry-aware convolutions. This approach enables more efficient and robust planning in complex environments, with applications ranging from robot navigation and manipulation to multi-robot coordination, impacting fields like robotics and autonomous systems.