Action Generation

Our research direction focuses on building a unified action-centric model for understanding and generation, tackling limited instruction generalization in open environments and misalignment between actions and visual signals. By integrating action generation, intent understanding, and intent generalization into a unified generative model, robots can better understand, generate, and generalize intents, enabling flexible action execution and robust instruction following in open-world scenarios.