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PRISM: Improving Robot Motor Control Policy Learning with Polynomial Representations

PRISM (Polynomial Representations for Interaction Structured Motor Control) is a physics-inspired policy representation method that learns multiplicative interactions among variables by recursively adding scaled bilinear features. It only requires replacing the first affine layer of a multilayer perceptron (MLP) and can be used for reinforcement learning and imitation learning without changing training objectives, sensing, or controllers.

Part of the event: PRISM Proposes Polynomial Representation for Interactive Structured Motion Control

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