In public and social spaces shared by humans and robots, it is essential for both parties to be aware of each other’s goals and to communicate their intentions effectively. This work in progress aims to develop a motion planner that not only finds the shortest path to a goal while adhering to social norms and spatial constraints but also generates communicative gestures and movements, such as hesitations or prompting motions. Extensive datasets are required to teach robots socially compliant navigation and effective communication with their human counterparts. Here, we outline our data collection efforts to train such a planner and to inform the broader community about potential interactions between humans and robots in these scenarios. The primary goal of this research is to establish a foundation for user-centered design of interaction strategies and avoidance mechanisms during social navigation.

Social Robots in human environments need to be able to reason about their physical surroundings while interacting with people. Furthermore, human proxemics behaviours around robots can indicate how people perceive the robots and can inform robot personality and interaction design. Here, we introduce Charlie, a situated robot receptionist that can interact with people using verbal and non-verbal communication in a dynamic environment, where users might enter or leave the scene at any time. The robot receptionist is stationary and cannot navigate. Therefore, people have full control over their personal space as they are the ones approaching the robot. We investigated the influence of different apparent robot personalities on the proxemics behaviours of the humans. The results indicate that different types of robot personalities, specifically introversion and extroversion, can influence human proxemics behaviours. participants maintained shorter distances with the introvert robot receptionist, compared to the extrovert robot. Interestingly, we observed that human-robot proxemics were not the same as typical human-human interpersonal distances, as defined in the literature. We therefore propose new proxemics zones for human-robot interaction.

Neeraj Cherakara, Finny Varghese, Sheena Shabana, Nivan Nelson, Abhiram Karukayil, Rohith Kulothungan, Mohammed Afil Farhan, Birthe Nesset, Meriam Moujahid, Tanvi Dinkar, Verena Rieser, Oliver Lemon. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.

We present a demonstration of a Robo-Barista: a social robot that takes hot beverage orders through verbal interaction and completes them via a Bluetooth enabled coffee machine. The demonstration is highly robust and it is the intention that this could be installed as a permanent feature, enabling “In the Wild” experimentation and long term studies. In the demonstration video, we show a user interacting with a Furhat robot to order a coffee. The robot has a novel architecture that allows it to exhibit both verbal and non-verbal cues, such as shared attention and chitchat. Furthermore, it is enabled with a unique tiredness detector based on visual facial features.

We introduce a situated interactive robot receptionist that can coordinate turn-taking and handle multi-party engagement and dialogue in dynamic environments, where users might enter or leave the scene at any time. The objective is to create a multi-user engagement policy to manage turn-taking using the robot's gaze, head pose, and verbal communication as parameters and to analyse the participant's perception of the robot. Participant feedback on the system was collected using an online survey that allowed for a comparison of subjective feedback for 4 different interaction policies. The results confirm the hypothesis that a robot is perceived as more intelligent and conscious when it reacts using eye gaze or head pose, once a new user enters the scene. Furthermore, we find that robots need to use a combination of verbal and non-verbal cues to coordinate turn-taking, in order to be perceived as polite and aware of human social norms.

We present a demonstration of a Robot Receptionist: a situated interactive robot that can coordinate turn-taking and handle multi-party engagement and dialogue in dynamic environments, where users might enter or leave the scene at any time. We use a Furhat robot, which is highly expressive and can use verbal communication as well as non-verbal cues, such as facial expressions. The system demonstrated and described here is composed of several modules, including scene analysis, engagement policies, and a dialogue manager.

As robots take on roles in our society, it is important that their appearance, behaviour and personality are appropriate for the job they are given and are perceived favourably by the people with whom they interact. Here, we provide an extensive quantitative and qualitative study exploring robot personality but, importantly, with respect to individual human traits. Firstly, we show that we can accurately portray personality in a social robot, in terms of extroversion-introversion using vocal cues and linguistic features. Secondly, through garnering preferences and trust ratings for these different robot personalities, we establish that, for a Robo-Barista, an extrovert robot is preferred and trusted more than an introvert robot, regardless of the subject’s own personality. Thirdly, we find that individual attitudes and predispositions towards robots do impact trust in the Robo-Baristas, and are therefore important considerations in addition to robot personality, roles and interaction context when designing any human-robot interaction study.