# PRISM: Improving Robot Motor Control Policy Learning with Polynomial Representations

- Source: arXiv cs.RO
- Published: 2026-10-19T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/s6fbg4ny9dmdksfvuxxxfc01a
- Original: https://arxiv.org/abs/2607.23473

## Summary

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.