With the increasing presence of robots in our daily lives, it is crucial to design interaction interfaces that are natural, easy to use and meaningful for robotic tasks. This is important not only to enhance the user experience but also to increase the task reliability by providing supplementary information. Motivated by this, we propose a multi-modal framework consisting of multiple independent modules. These modules take advantage of multiple sensors (e.g., image, sound, depth) and can be used separately or in combination for effective human-robot collaborative interaction. We identified and implemented four key components of an effective human robot collaborative setting, which included determining object location and pose, extracting intricate information from verbal instructions, resolving user(s) of interest (UOI), and gesture recognition and gaze estimation to facilitate the natural and intuitive interactions. The system uses a feature-detector-descriptor approach for object recognition and a homography-based technique for planar pose estimation and a deep multi-task learning model to extract intricate task parameters from verbal communication. The user of interest (UOI) is detected by estimating the facing state and active speakers. The framework also includes gesture detection and gaze estimation modules, which are combined with a verbal instruction component to form structured commands for robotic entities. Experiments were conducted to assess the performance of these interaction interfaces, and the results demonstrated the effectiveness of the approach.
Keywords: HRI; collaborative HRI; gaze estimation; gesture recognition; human–AI interaction; information extraction; interaction interface; multi-modal inputs; multi-party interaction; pose estimation.