Survey of Physics-Embedded Priors for Improved Robot Learning

This survey examines methods for integrating physical laws and constraints directly into learning algorithms to compensate for limited robotic datasets. The approach aims to improve sample efficiency and generalization in real-world environments compared to purely data-driven methods.