The method adds polynomial interaction features to policy learning so controllers can capture products and relationships among state variables. Experiments cover humanoid locomotion, low-friction tracking, and contact-rich manipulation, showing improvements over standard and larger control baselines.
PRISM is useful for robot learning researchers, motor-control engineers, and teams building policies for complex physical interaction. It offers a structured representation layer that can reveal physical relationships while improving policy performance in challenging dynamics.
