Italy

With becoming an aging society rapidly, the digital divide among the elderly is expected to emerge as a social problem. In order to bridge the digital gap between the elderly and the general public, it is necessary to find out what difficulties the elderly have in using information and communication technology (ICT) and what technologies and services they need. In order to figure out those issues, we conducted three phases of study: ICT literacy survey, interview, and design workshop (N=10). This study has three major findings. First, from the physical view, the elderly found ICT that reduces their movement distance attractive. Second, from the cognitive perspective, services which reduce the gap between the real physical ability of the elderly and the perceived ability of them by themselves are needed. Lastly, from the psychological view, an intuitive and natural interface is needed for the elderly who are afraid of learning new technology.

IEEE RO-MAN 2022

Exploring ICT for the Elderly: By Analyzing the Elders’ Needs for Information Acquisition and Delivery

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

technical paper

Social robots have been shown effective in pedagogical settings due to their embodiment and social behavior that can improve a learner’s motivation and engagement. In this study, the impact of a social robot’s motivational gestures in robot-assisted language learning (RALL) was investigated. Twenty-five university students participated in a language learning task tutored by a NAO robot under two conditions (within-subjects design); in one condition the robot provided positive and negative feedback on participant’s performance using both verbal and non-verbal behavior (Gesture condition), in another condition the robot only employed verbal feedback (No-Gesture condition). To assess cognitive engagement and learning in each condition, we collected EEG brain activity from the participants during the interaction and evaluated their word knowledge during an immediate and delayed post-test. No significant difference was found with respect to cognitive engagement as quantified by the EEG Engagement Index during the practice phase. Similarly, the word test results indicated an overall high performance in both conditions, suggesting similar learning gain regardless of the robot’s gestures. These findings do not provide evidence in favor of robot’s motivational gestures during language learning tasks but at the same time indicate challenges with respect to the design of effective social behavior for pedagogical robots.

Motivational Gestures in Robot-Assisted Language Learning: A Study of Cognitive Engagement using EEG Brain Activity

Possible applications for Social Robots in healthcare settings, that could have a tremendous social impact in helping alleviating staff workload, are those of a patient-facing role such as robot receptionist, providing assistance to patients and visitors. 
Examples of functions that such robots would need to be able to execute are greeting visitors, reception check-in/out of patients, answering common questions they may have, showing them where to sit, helping them locate missing objects, providing directions to facilities, guiding them to different locations, etc. 
In this paper we describe current progress towards developing a multimodal conversational AI system integrated in a Social Conversational Robot (an ARI robot) that will act as a receptionist in a hospital waiting room.
We present the developed architecture of the system and report on an initial experimental validation study carried out in laboratory conditions with the ARI robot. 


Developing a Social Conversational Robot for the Hospital waiting room

We present SLOT-V, a novel supervised learning framework that learns observer models (human preferences) from robot motion trajectories in a legibility context. Legibility measures how easily a (human) observer can infer the robot's goal from a robot motion trajectory. When generating such trajectories, existing planners often rely on an observer model that estimates the quality of trajectory candidates. These observer models are frequently hand-crafted or, occasionally, learned from demonstrations. Here, we propose to learn them in a supervised manner using the same data format that is frequently used during the evaluation of aforementioned approaches. We then demonstrate the generality of SLOT-V using a Franka Emika in a simulated manipulation environment. For this, we show that it can learn to closely predict various hand-crafted observer models, i.e., that SLOT-V's hypothesis space encompasses existing handcrafted models. Next, we showcase SLOT-V's ability to generalize by showing that a trained model continues to perform well in environments with unseen goal configurations and/or goal counts. Finally, we benchmark SLOT-V's sample efficiency (and performance) against an existing IRL approach and show that SLOT-V learns better observer models with less data. Combined, these results suggest that SLOT-V can learn viable observer models. Better observer models imply more legible trajectories, which may - in turn - lead to better and more transparent human-robot interaction.

SLOT-V: Supervised Learning of Observer Models for Legible Robot Motion Planning in Manipulation

Haptics is an essential element of interaction between humans and socially assistive robots. However, it is often limited to movements or vibrations and misses key aspects such as temperature. This mixed-methods study explores the potential of enhancing human-robot interaction [HRI] through thermal stimulation to regulate affect during a stress-inducing task. Participants were exposed to thermal stimulation while completing the Mannheim-multicomponent-stress-task (MMST). Findings yielded that human-robot emotional touch may induce comfort and relaxation during the exposure to acute stressors. User affect may be further enhanced through thermal stimulation, which was experienced as comforting, de-stressing, and altered participants’ perception of the robot to be more life-like. Allowing participants to calibrate a temperature they perceived as calming provided novel insights into the temperature ranges suitable for interaction. While neutral temperatures were the most popular amongst participants, findings suggest that cool (4 – 29 ºC), neutral (30 – 32 ºC), and warm (33ºC -36 ºC) temperatures can all induce comforting effects during exposure to stress. The results highlight the potential of thermal HRI in general and, more specifically, the advantages of personalized temperature calibration.


Hot or not? Exploring User Perceptions of thermal Human-Robot Interaction

Constructs intended to capture social attitudes and behaviour towards social robots are incredibly varied, with little overlap or consistency in how they may be related. In this study we conduct exploratory analyses between participants' self-reported attitudes and behaviour towards a social robot. We designed an autonomous interaction where 102 participants interacted with a social robot (Pepper) in a hypothetical travel planning scenario, during which the robot displayed various multi-modal social behaviours. Several behavioural measures were embedded throughout the interaction, followed by a self-report questionnaire targeting participant's social attitudes towards the robot (social trust, liking, rapport, competency trust, technology acceptance, mind perception, social presence, and social information processing). Several relationships were identified between participant's behaviour and self-reported attitudes towards the robot. Implications for how to conceptualise and measure interactions with social robots are discussed.

Does what users say match what they do? Comparing self-reported attitudes and behaviours towards a social robot

Emotions have always played a crucial role in human evolution, improving not only social contact but also their ability to adapt and react to a changing environment. In the field of social robotics, providing robots with the ability to recognize human emotions through the interpretation of non-verbal signals may represent the key to more effective and engaging interaction. However, the problem of emotion recognition has usually been addressed in limited and static scenarios, by classifying emotions using sensory data such as facial expressions, body postures, and voice. This work proposes a novel emotion recognition framework, based on the appraisal theory of emotion. According to the theory, the
expected person’s appraisal of a given situation depending on their needs and goals (henceforth referred to as “appraisal information”) is combined with sensory data. A pilot experiment was designed and conducted: participants were involved in spontaneous verbal interaction with the humanoid robot Pepper, programmed to elicit different emotions in various
moments. Then, a Random Forest classifier was trained to classify positive and negative emotions using: (i) sensor data only; (ii) sensor data supplemented by appraisal information. Preliminary results confirm a performance improvement in emotion classification when appraisal information is considered.

Assessing Emotions in Human-Robot Interaction Based on the Appraisal Theory

As robotic systems become more interactive and complex, there is a need to standardise interfaces and simplify development processes. This is particularly pertinent in the field of manufacturing, where human-robot collaboration is on the increase, but where standards and proprietary software are key barriers to deployment and adoption. In this article we present the ROSIE Adapter, a generalpurpose, modular adapter developed in ROS designed to support the creation and connection of industry-ready digital twins. Together with our previous work on the modular CSI digital twin framework, we demonstrate how the ROSIE Adapter creates a versatile “plug-and-play” interface that simplifies the development of new robotic processes, and improves accessibility to novice users. Furthermore, the adaptor supports integration of intuitive interface devices, such as speech and augmented reality interfaces, which enable more natural collaboration. We describe the adaptor and its use in two real-world applications, demonstrate the ease of use via a three-day hackathon event, and provide results showing the faithfulness of the arising digital twins to their connected physical systems.

ROSIE: A ROS adapter for a Modular Digital Twinning Framework

To assist human users according to their individual preference in assembly tasks, robots typically require user demonstrations in the given task. However, providing demonstrations in actual assembly tasks can be tedious and time-consuming. Our thesis is that we can learn the preference of users in actual assembly tasks from their demonstrations in a representative canonical task. Inspired by prior work in economy of human movement, we propose to represent user preferences as a linear reward function over abstract task-agnostic features, such as movement and physical and mental effort required by the user. For each user, we learn the weights of the reward function from their demonstrations in a canonical task and use the learned weights to anticipate their actions in the actual assembly task; without any user demonstrations in the actual task. We evaluate our proposed method in a model-airplane assembly study and show that preferences can be effectively transferred from canonical to actual assembly tasks, enabling robots to anticipate user actions.

Human-Guided Goal Assignment to Effectively Manage Workload for a Smart Robotic Assistant

Managing robot workloads in human robot teams is critical for efficient team operation. If robots are overloaded with work, then they will miss deadlines and force humans to take on extra work. This paper presents a framework for a robot to assess its own workload based on an initial goal assignment. The robot does this by generating task and motion plans and computing the probability of missing deadlines due to the possibility of delays in task execution. A branch and bound based search is used to generate task and motion plans by minimizing task execution effort. The robot presents a diverse set of task and motion plans to the humans to offer multiple different options. Humans can either approve a plan or provide guidance to reduce the workload by either relaxing deadlines or removing goal(s) assigned to the robots.

Who’s on First? The Impact of the Proactive Interaction on User Acceptance of the Robotized Object

Due to advances in technology, various types of existing products can be robotized. The interaction design between the robotized objects which are the robotized products and users has become an emerging issue since how to interact affects user acceptance of the new products. Because the robots can perceive, recognize and assist the users, robotized objects can assist users by reading the context and guessing their intentions in advance, or can assist users in response to users’ clear requests. In order to explore the effective interaction strategy of robotized object, we conduct 2 × 2 mixed-participant experiment. As a result, participants were more surprised by and felt positive emotion toward robotized objects with proactive interaction than with reactive interaction. In addition, the robotized object with proactive interaction was evaluated as more intelligent and easier to use than that with reactive interaction. The influence of interaction strategies on satisfaction of robotized objects was mediated by surprise and positive emotions. Furthermore, the robotized object with proactive interaction was evaluated as more positive when the user’s intention was expressed only by action cue than when it was expressed by both verbal cue and action cue.

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