Long Horizon Trajectory

Long-horizon trajectory prediction and planning focuses on generating sequences of future states for autonomous agents, robots, or even wildlife, extending far beyond immediate timesteps. Current research emphasizes efficient algorithms that balance computational cost with accuracy, employing techniques like generative models (including deep ensembles and variational autoencoders), diffusion-based methods guided by temporal logic constraints, and optimization approaches such as mixed-integer linear programming. These advancements are crucial for safe and effective autonomous navigation, robotic manipulation, resource-constrained exploration, and even ecological modeling, enabling more robust and adaptable systems in diverse applications.