RoboAnnotatorX: A Comprehensive and Universal Annotation Framework for Accurate Understanding of Long-Horizon Robot Demonstration

Recent advances in robotics have produced numerous valuable large-scale demonstration datasets, yet their potential remains underutilized due to annotation limitations. Current datasets often suffer from sparse temporal annotations and inconsistent labeling granularity, particularly for complex long-horizon demonstrations. Traditional manual annotation methods are expensive and poorly scalable while existing automated methods struggle with temporal coherence and semantic richness across extended demonstrations. For this, we propose RoboAnnotatorX, a reliable annotation tool that enhances multimodal large language model to generate high-quality, context-rich annotations for complex long-horizon demonstrations. Specifically, we introduce a multi-scale token-efficient encoder