Scene Generation

Our research direction aims to build a unified robot world model for navigation and manipulation, addressing limited long-horizon action generation, weak continuous instruction understanding, and the difficulty of jointly training navigation and manipulation data. By integrating long-horizon generation, continuous instruction understanding, and cross-modal data fusion into a unified model, we enable robots to understand, transition, and execute continuous intents, improving long-horizon task handling and autonomous decision-making in complex scenes.