In this work, we consider the use of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to assist the communication between the access point (AP) and two users, along with the existence of direct links. Considering the non-orthogonal multiple access (NOMA) based transmission by AP, we derive the expressions for coverage probability of both the users assuming Nakagami-m fading channels with the possibility of imperfect successive interference cancellation (SIC) at the near user. We also find the optimal values of NOMA power allocation parameter and STAR-RIS transmission coefficient that jointly minimizes the absolute difference in the throughput of the two users. The superiority of the proposed system is shown through numerical results in contrast to a STAR-RIS assisted orthogonal multiple access (OMA) transmission and a conventional NOMA scheme. The impact of various other system parameters like number of reflecting elements and fraction of imperfect SIC is demonstrated on the system performance.

With more social robots entering different industries such as educational systems, health-care facilities, and even airports, it is important to tackle problems that may hinder high quality interactions in a wild setting including group conversations. This paper presents an autonomous group conversational role coordinator system based on the proxemics of participants in the group with the robot including their distances and orientations. The system accordingly assigns to the group participants around the robot three different statuses: active, bystander, and overhearer. Once the statuses are estimated, the robot autonomously adjusts its gaze pattern in order to adapt to the group dynamics and attributes its attention in relation to the role the member in the group is playing. This system was evaluated through a pilot study (N=16), in which two participants at a time played a trivia game with the robot and had different roles to play within the interaction. The primary results imply that the participants interacting with a robot having this adaptive gaze behavior based on conversational role coordination are more likely to stand closer to the robot. In addition, the robot was perceived as more adaptable, sociable, and socially present as well as more likely to make the participants feel more attended to.

The video describes the novel concept of “culturally competent robotics”, which is the main focus of the project CARESSES (Culturally-Aware Robots and Environmental Sensor Systems for Elderly Support). CARESSES a multidisciplinary project whose goal is to design the first socially assistive robots that can adapt to the culture of the older people they are taking care of. Socially assistive robots are required to help the users in many ways including reminding them to take their medication, encouraging them to keep active, helping them keep in touch with family and friends. The video describes a new generation of robots that will perform their actions with attention to the older person's customs, cultural practices and individual preferences.

This video illustrates the large-scale experiment of the L2TOR project that will be presented at the HRI 2019 conference. The experiment aimed to investigate how 192 Dutch 5-year-old children could learn 34 English words from a NAO robot in 7 lessons. The experiment compared 4 conditions: 1) robot using iconic gestures, 2) robot without iconic gestures, 3) tablet only, and 4) a control group. The results revealed that children could learn more English words in all experimental conditions compared to the control group. The three experimental conditions did not show any significant differences regarding the learning outcomes.

We present a large-scale study of a series of seven lessons designed to help young children learn English vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot's iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot's iconic gestures on learning gains.

Designing reliable, humanoid social robots with highly expressive human-like faces is a challenging problem. Their construction requires complex mechanical assemblies, large numbers of actuators and involves soft material. When deploying these robots in the field they face problems of wear and tear and mechanical abuse. Mechanical defects of such faces can be hard to analyze automatically or by manual visual inspection. We propose a method of automatic visual calibration and actuator fault detection for complex animatronic faces. We use our approach to scan three expressive animatronic faces, and analyze the data. Our findings indicate that our approach is able to detect faulty actuators even when they contribute to the overall expression of the face only marginally, and are hard to spot visually.

Cultural competence is a well known requirement for an effective healthcare, widely investigated in the nursing literature. We claim that personal assistive robots should likewise be culturally competent, aware of general cultural characteristics and of the different forms they take in different individuals, and sensitive to cultural differences while perceiving, reasoning, and acting. Drawing inspiration from existing guidelines for culturally competent healthcare and the state-of-the-art in culturally competent robotics, we identify the key robot capabilities which enable culturally competent behaviours and discuss methodologies for their development and evaluation.

A central component of the human self is the narrative history of shared interactions with others, which provides the foundation for social relations that develop over extended time. The loss of this narrative self progressively becomes catastrophic for aging subjects with degenerative disease of the memory system. A prosthetic device for narrative memory can provide an at least temporary solution to this problem. We identify requirements for a narrative memory capability that can allow individuals with diminished memory to continue to interact socially with partners with whom they have shared experiences. A memory prosthetic should provide access to past memories of the subject, and should accompany the subject in the formation, organization and retrieval of new memories. Based on these requirements, we have implemented the V1.0 narrative memory companion on the Pepper humanoid robot using the native Choregraph and NAOqi system capabilities. We exploit principals developed in our research in autobiographical memory and the organization of experience in cooperative humanoid robots, and the mapping of narrative structure onto this experience. In the narrative companion, past memories are first collected from the subject or members of their entourage via a template-based interview, and a small number of photographs that illustrate important people and events in the subject's past. New memories constructed via interaction with Pepper, and by simple narratives told by the human partner, are stored in the Autobiographical Memory (ABM) implemented in the ALKnolwedge base of the NAOqi system. Memories are then recalled and shared by narrative. Results from a naïve case study are presented, and future applications for improved quality of life are discussed.

Allowing humans to teach robot behaviors will facilitate acceptability as well as long-term interactions. Humans would mainly use speech to transfer knowledge or to teach highlevel behaviors. In this paper, we propose a proof-of-concept application allowing a Pepper robot to learn behaviors from their natural-language-based description, provided by naive human users. In our model, natural language input is provided by grammar-free speech recognition, and is then processed to produce semantic knowledge, grounded in language and primitive behaviors. The same semantic knowledge is used to represent any kind of perceived input as well as actions the robot can perform. The experiment shows that the system can work independently from the domain of application, but also that it has limitations. Progress in semantic extraction, behavior planning and interaction scenario could stretch these limits.

For intelligent Human-Robot Interaction (HRI), a robot should be equipped with some core reasoning capabilities such as perspective taking, effort analysis, and affordance analysis. This paper starts to explore how a robot equipped with such reasoning abilities could be evaluated. To this end, inspired by the Turing test, we design a game involving a human-robot competition scenario. Interestingly, the participants' subjective feedback, which tended to compare the robot's abilities with their own, points toward potential criteria for developing benchmark scenarios and evaluation matrices.

Bridging the gap between symbolic and geometric planning has received much attention in recent years. An important issue in some of the works that combine the two approaches is finding the right balance between backtracking at the symbolic level versus at the geometric planning level. We present in this work a new approach to interleaved backtracking, where the symbolic planner backtracks to try alternative action branches that naturally map to different geometric solutions. This eliminates the need to “protect” certain symbolic conditions when backtracking at the geometric level, and addresses a completeness issue in our previous approach to interleaved backtracking. We discuss a concrete, non-trivial symbolic-geometric planning example in the context of Human-Robot Interaction, a full implementation of the combined planning technique, and an evaluation of performance as well as the effect of increasing the symbolic-action branching factor.

Analyzing affordances has its root in socio-cognitive development of primates. Knowing what the environment, including other agents, can offer in terms of action capabilities is important for our day-to-day interaction and cooperation. In this paper, we will merge two complementary aspects of affordances: from agent-object perspective, what an agent afford to do with an object, and from agent-agent perspective, what an agent can afford to do for other agent, and present a unified notion of Affordance Graph. The graph will encode affordances for a variety of tasks: take, give, pick, put on, put into, show, hide, make accessible, etc. Another novelty will be to incorporate the aspects of effort and perspective-taking in constructing such graph. Hence, the Affordance Graph will tell about the action-capabilities of manipulating the objects among the agents and across the places, along with the information about the required level of efforts and the potential places. We will also demonstrate some interesting applications.

While symbolic planners work with an abstract representation of the real world, allowing plans to be constructed relatively quickly, geometric planning - although more computationally complex - is essential for building symbolic plans that actually work in the real world. To combine the two types of systems, we present in this paper a meaningful interface, and insights into a methodology for developing interwoven symbolic-geometric domains. We concretely present this “link” between the two approaches with algorithms and data structures that amount to an intermediate layer that coordinates symbolic-geometric planning. Since both planners are capable of “backtracking” at their own levels, we also investigate the issue of how to interleave their backtracking, which we do in the context of the algorithms that form the link. Finally, we present a prototype implementation of the combined system on a PR2 robot.

Affordance analysis, what something/someone can afford or offers, is an important aspect for day-to-day interaction and decision-making. In this paper, we will enrich the notion of affordance by incorporating agent-agent affordance: what does an agent afford for another agent in terms of a task. Further, we will present an effort hierarchy and derive the concept of Taskability Graph, which encodes: what all agents could do for all other agents, with which levels of mutual-efforts and at which places. This makes the robot more aware about agents' abilities and facilitates to develop better interaction and decision-making capabilities. We will discuss the potential application in effort based shared cooperative planning.

Interestingly Humans are able to maintain rough estimations of visibility, reachability and other capabilities of not only themselves but of the person they are interacting with. Studies in neuroscience and psychology suggest that from the age of 12-15 months children start to understand the occlusion of others line-of-sight and from the age of 3 years they start to develop the ability, termed as perceived reachability for self and for others. As such capabilities evolve in the children, they start showing intuitive and proactive behavior by perceiving various abilities of the human partner. Inspired from such studies, which suggest that visuo-spatial perception plays an important role in Human-Human interaction, we propose to equip our robot with the capabilities to maintain various types of reachabilities and visibilities information of itself and of the human partner in the shared workspace. Since these analyses will be basically perceived by performing a virtual action onto the agent and roughly estimating what that agent might be able to 'see' and 'reach' in 3D space, we term these representations as Mightability Maps. By applying various set operations on Weighted Mightability Maps, robot could perceive a set of candidate solutions in real time for various tasks. We show its application in exhibiting two different behaviors of robot: co-operative and competitive. These maps are also quick to compute and could help in developing higher-level decisional capabilities in the robot.

For a Mobile Robot to navigate in the Human-Centered environment without imposing alien like impression by its motion, it should be able to reason about various criteria ranging from clearance, environment structure, unknown objects, social conventions, proximity constraints, presence of an individual or group of peoples, etc. Also the robot should neither be over-reactive nor be simple wait and move machine. We have adapted a Voronoi diagram based approach for the analysis of local clearance and environment structure. We also propose to treat human differently from other obstacles for which the robot constructs different sets of regions around human and iteratively converges to a set of points (milestones), using social conventions, human proximity guidelines and clearance constraints to generate and modify its path smoothly. Once equipped with such capabilities, robot is able to do higher-level reasoning for dynamic and selective adaptation of social convention depending upon the environment segment. It also leads the robot to be aware about its own motion behavior.