Control Input Inference

Control input inference aims to determine the actions or control signals underlying observed system behavior, often in complex multi-agent scenarios or robotic systems. Current research focuses on developing robust algorithms that infer control inputs even with incomplete knowledge of system objectives, leveraging techniques like state-space modeling, Gaussian processes, and graph neural networks to improve prediction accuracy and handle uncertainty. These advancements are significant for improving the safety and efficiency of autonomous systems, enabling better trajectory prediction and more effective learning from demonstrations in robotics, and enhancing security by detecting malicious control actions.