Trajectory Refinement

Trajectory refinement focuses on improving the accuracy and efficiency of predicted or generated robot trajectories, addressing challenges like generalization, robustness to noise, and computational cost. Current research emphasizes methods leveraging diffusion models, transformers, and neural ordinary differential equations to refine trajectories based on various sources of information, including demonstrations, sensor data, and high-level commands. These advancements are crucial for improving the performance of autonomous systems in diverse applications, such as robotics, autonomous driving, and traffic simulation, by enabling more accurate and reliable motion planning and control.