COntinuous Trajectory

Continuous trajectory research focuses on modeling and predicting the continuous evolution of systems over time, aiming to improve accuracy and efficiency compared to discrete-time approaches. Current efforts leverage various neural network architectures, including neural ordinary differential equations, transformers, and recurrent networks, often coupled with optimization algorithms like DDPG or B-spline methods, to learn and generate continuous trajectories from diverse data sources (e.g., point clouds, sensor readings, event sequences). These advancements find applications in diverse fields, such as robotics (trajectory planning and control), traffic surveillance (multimodal data fusion), and the modeling of complex physical systems (e.g., active matter). The ability to accurately represent and predict continuous trajectories has significant implications for improving the performance and robustness of autonomous systems and enhancing our understanding of complex dynamical processes.