Agent Motion

Agent motion research focuses on understanding and predicting the movement of multiple entities, whether robots, humans, or vehicles, within shared environments. Current efforts concentrate on developing robust and efficient models, employing architectures like transformers and state space models, to handle diverse scenarios including constrained spaces and complex social interactions. These advancements are crucial for improving autonomous systems, human-robot collaboration, and the simulation of realistic multi-agent behaviors in various applications, from autonomous driving to sports analytics. The field is actively pursuing unified frameworks capable of addressing trajectory prediction, imputation, and recovery simultaneously, leading to more accurate and adaptable models.