Leveraging foundation models for scene understanding in human-robot teaming

The evolution of robots from tools to teammates will require them to derive meaningful information about the world around them, translate knowledge and skill into effective planning and action based on stated goals, and communicate with human partners in a natural way. Recent advances in foundation models, large pre-trained models such as large language models and visual language models, will help enable these capabilities. We describe how we are using open-vocabulary 3D scene graphs based on foundation models to add scene understanding and natural language interaction to our human-robot teaming research. Open-vocabulary scene graphs enable a robot to build and reason about a semantic map of the environment, as well as answer complex queries about it. We are exploring how semantic scene information can be shared with human teammates and inform context-aware decision making and planning to improve task performance and increase autonomy. We highlight human-robot teaming scenarios involving robotic casualty evacuation and stealthy movement through an environment that could benefit from enhanced scene understanding, describe our approach to enabling this enhanced understanding, and present preliminary results using a one-armed quadruped robot interacting with simplified environments. It is anticipated that advanced perception and planning capabilities provided by foundation models will give robots the ability to better understand their environment, share that information with human teammates, and generate novel courses of action.