Diffusion Planner

Diffusion planners leverage diffusion models to generate trajectories for robotic control and decision-making, aiming to overcome limitations of traditional planning methods in complex, high-dimensional environments. Current research focuses on improving the efficiency and reliability of these planners, often employing hierarchical architectures, incorporating reinforcement learning for fine-tuning, and developing techniques to address issues like infeasible trajectory generation and limited generalization. This approach holds significant promise for advancing robotics, particularly in tasks requiring long-horizon planning, adaptability to unforeseen circumstances, and safe operation in dynamic environments.