# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### Multi-Modality in Multi-User Conversational Interfaces
#### 4.3.2 Robotic Agents  
Similar to the application area of service robots, embodied interfaces play an important role in MUCIs. [PERSON] et al. [49] present a large language model-based spoken dialogue system deployed on a social robot, facilitating conversations with multiple patients and their companions in a hospital setting. The system processes both speech and video inputs and generates multi-modal outputs, including speech and gestures (arm, head, and eye movements), to manage turn-taking, handle in-domain questions, and respond appropriately to out-of-domain requests. The authors describe the system's architecture and components, highlighting its ability to generate human-like clarification requests and adapt to unexpected patient utterances, with a demonstration video showcasing the system's real-time operability. [PERSON] et al. [50] introduce a system designed for multi-party communication with adaptable gestures and facial expressions based on a speaker's location and situation. It utilises a special database for real-time generation of flexible gestures, combined with speech generation and gaze motion planning. The system incorporated sensory data, including the identification of speakers, using a camera, and featured motion parametrisation and gaze motion planning. In the paper of [PERSON] et al. [51], the ROCARE system is described, designed to facilitate multi-user engagement-based coaching. ROCARE aimed to adapt its behaviour to individual users' progress and assess user task performance, affective states, and gaze positions. It employed a modular architecture, including sensing, actuation, database, supervisory controller, and graphical user interface, with an emphasis on multi-modal human-robot interaction. Moreover, [PERSON] et al. [10] focused on a dialogue-based robot service that could provide relevant services to multiple users simultaneously. Their service-scheduling scheme analysed user intention based on factors like distance from the robot and vocal requests. This scheme allowed the robot to generate user-specific services and handle interactions with up to three users concurrently, recognizing and analysing their intentions. To address socio-feedback in MUCIs, [PERSON] et al. [35] integrated and analysed non-verbal speech metrics to assess the social states of participants in two-person conversations with a humanoid Nao robot. The robot used this information to provide appropriate feedback in real-time, improving the quality of interactions. Speech mannerism and sociometrics, including interest, agreement, and dominance of the speakers, were quantified through non-verbal speech metrics. [PERSON] et al. [52] described the Tangy robot and its use in facilitating multi-step group recreational activities. Tangy utilised multi-modal interactions, including gesture mimicry, speech interaction, and a chest-mounted tablet for display. It retrieved world state parameters through various sensors, such as cameras and laser range finders, and was capable of real-time behaviour adaptation to accommodate user-specific activities. In [PERSON] et al. [53], the authors introduced an augmented reality (AR)-based system designed for human-robot collaborative environments. This system allowed workers to interact with a robot while receiving information related to the state of the robot and safety plans. For this, the system employed a heavy-duty mobile platform integrated with an arm manipulator, along with a Microsoft HoloLens for AR interaction. It tracked the user's head gaze, recognised hand gestures, and provided information about the robot's intended movements and navigation plans for enhanced safety and trust. [PERSON] et al. [21] proposed a social multi-user interaction model using a robot bartender system. The system used vision- and speech-processing modules, along with a behaviour controller. It maintained a model of the social context to generate effective and socially appropriate responses based on observations about users in the environment.