Researchers have developed a method to improve cross-embodiment transfer in latent action models (LAMs) using action-similarity supervision. This approach allows LAMs to learn from demonstrations recorded by one robot and apply them to another robot, reducing the need for costly demonstrations. The method was evaluated on RoboTwin 2.0 and showed significant improvements in cross-embodiment transfer.