Hierarchical Policies from Verbal and Egocentric Human Signals for Natural Human-Robot Interaction

The paper presents EDITH, a novel robot framework designed for natural human-robot interaction by integrating both verbal and nonverbal signals, such as gestures and gaze, captured through smart glasses. EDITH employs a hierarchical policy architecture where a high-level policy interprets human intent and generates subtasks, while a low-level policy executes these tasks, demonstrating improved performance in interactive tasks by reducing reliance on language instructions alone. This approach enhances the robot's ability to understand and respond to human intent, which is crucial for developing more intuitive and efficient human-robot collaboration systems.