PRISM: Polynomial Representations for Interaction-Structured Motor Control
PRISM introduces a new way for robot policies to learn complex physical interactions by explicitly representing polynomial relationships among observable variables. Instead of implicitly learning these interactions through standard MLPs, PRISM uses a recursive structure to add scaled bilinear features. This method allows the policy to capture higher-order physical dependencies efficiently, leading to significant performance gains in locomotion and manipulation while maintaining plug-and-play compatibility with existing reinforcement learning frameworks.