Kinematic Prior

Kinematic priors represent pre-existing knowledge about the movement capabilities and constraints of a system, such as a robot or human body, often encoded as models of its physical structure and dynamics. Current research focuses on integrating these priors into machine learning models, particularly deep reinforcement learning and transformer networks, to improve the accuracy, robustness, and efficiency of tasks like motion planning, trajectory prediction, and 3D pose estimation. This approach enhances performance in various applications, including humanoid robotics, autonomous driving, and animation, by guiding learning processes and ensuring physically plausible outputs, even with limited data or in challenging environments.