EgoDemoGen: Novel Egocentric Demonstration Generation Enables Viewpoint-Robust Manipulation (2509.22578v1)

Abstract: Imitation learning based policies perform well in robotic manipulation, but they often degrade under egocentric viewpoint shifts when trained from a single egocentric viewpoint. To address this issue, we present EgoDemoGen, a framework that generates paired novel egocentric demonstrations by retargeting actions in the novel egocentric frame and synthesizing the corresponding egocentric observation videos with proposed generative video repair model EgoViewTransfer, which is conditioned by a novel-viewpoint reprojected scene video and a robot-only video rendered from the retargeted joint actions. EgoViewTransfer is finetuned from a pretrained video generation model using self-supervised double reprojection strategy. We evaluate EgoDemoGen on both simulation (RoboTwin2.0) and real-world robot. After training with a mixture of EgoDemoGen-generated novel egocentric demonstrations and original standard egocentric demonstrations, policy success rate improves absolutely by +17.0% for standard egocentric viewpoint and by +17.7% for novel egocentric viewpoints in simulation. On real-world robot, the absolute improvements are +18.3% and +25.8%. Moreover, performance continues to improve as the proportion of EgoDemoGen-generated demonstrations increases, with diminishing returns. These results demonstrate that EgoDemoGen provides a practical route to egocentric viewpoint-robust robotic manipulation.