Italy

During the COVID-19 pandemic, many research areas that require in person experiments with human volunteers have been impacted due to lockdowns and other activity-restricting policies. The field of robotics is no exception, and specially human-robot interaction research has been severely impacted. In order to circumvent the difficulty of gathering volunteers in person to interact with a robot, we have decided to build a novel crowdsourcing web platform for hosting our &quot;Talk to Kotaro&quot; experiment. The experiment consists of volunteers talking to a robot avatar and reacting to its semantic-free utterances. The developed web platform, which was built using the Python Flask framework, allows for such interactions while recording audio and video and other relevant data, which will be used for studying human impression estimation on gibberish speech. This paper describes not only the experiment and its preliminary results, but the developed platform itself; such tool is essential during pandemics and very useful for regular times, because it enables crowdsourcing data from all over the world.

IEEE RO-MAN 2022

How a Social Robot&#39;s Vocalization Affects Children&#39;s Speech, Learning, and Interaction

**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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[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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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.

During the COVID-19 pandemic, many research areas that require in person experiments with human volunteers have been impacted due to lockdowns and other activity-restricting policies. The field of robotics is no exception, and specially human-robot interaction research has been severely impacted. In order to circumvent the difficulty of gathering volunteers in person to interact with a robot, we have decided to build a novel crowdsourcing web platform for hosting our "Talk to Kotaro" experiment. The experiment consists of volunteers talking to a robot avatar and reacting to its semantic-free utterances. The developed web platform, which was built using the Python Flask framework, allows for such interactions while recording audio and video and other relevant data, which will be used for studying human impression estimation on gibberish speech. This paper describes not only the experiment and its preliminary results, but the developed platform itself; such tool is essential during pandemics and very useful for regular times, because it enables crowdsourcing data from all over the world.

How a Social Robot's Vocalization Affects Children's Speech, Learning, and Interaction

technical paper

Safety is a fundamental requirement in any human-robot collaboration scenario. To ensure the safety of users, from both physical and psychological aspects, for such scenarios, we propose a novel Virtual Barrier system facilitated by an augmented reality interface. Our system provides two types of Virtual Barriers to ensure safety: 1) a Virtual Person Barrier which encapsulates and follows the user to protect them from collisions with the robot, and 2) Virtual Obstacle Barriers which users can spawn to protect objects or regions that the robot should not enter. Our system utilizes augmented reality to visually display these protective barriers to the user during operation. To enable effective human-robot collaboration, our system automatically replans the robot's motion when potential collisions are detected as a result of a barrier intersecting the robot's planned path. We compared our novel system with a standard 2D display interface through a user study, where participants performed a task mimicking an industrial manufacturing procedure. Results show that our system increases both physical and psychological safety and task efficiency, and makes the interaction more intuitive.


Explain yourself! Effects of Explanations in Human-Robot Interaction

Passing pedestrians is a common task for pairs while moving. Whilst pairs generally prefer Side-by-side walking mode, that mode tends to occupy more space in the pathway and reduces space for pedestrians traveling in the opposite direction than Leader-Follower mode in which one follows the other. Thus, humans often intuitively consider solutions to optimize the balance between side-by-side walking mode and moving space for others in passing. This is also a problem that designers of companion robots often have to solve. By discovering, modeling, and incorporating a new factor - the habit of moving with the flow and density in moving (called dynamic density) - this work proposes a novel model to determine natural navigation pathways for companion robot to pass multiple pedestrians walking in the opposite directions, mimicking human passing behaviors by taking into account this factor. Based on two experimental observations and data collections, the model was developed and then validated by comparing the pathways generated by the model and the natural moving plans of the pairs in the same situations. The simulation results show that the new model is able to determine moving plans of pairs in passing situations, similar to real decisions of humans.

Sex doll as a new form of transitional object

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.

Parental Responses to Aggressive Child Behavior towards Robots, Smart Speakers, and Tablets

There is a growing interest in the retail industry to deploy service robots for customer interactions. Deploying such customer-facing robots raises the question of how we want to interact with these robots and reveals concerns that businesses and marketers could use robots to manipulate consumers. In this experiment, 67 study participants interacted with different virtual shopping robots that tried to impact "shoppers" purchasing decisions. The results indicate that a robot can increase consumer spending. The study exemplifies how a collaborative robot could be used as a customer-serving robot in a retail environment and investigates the impact of (i) different interaction strategies (human vs robot control) and (ii) dark patterns on shopping behavior (manipulative vs supportive robot).

Learning Musical Instrument with the Help of Social Robots: Attitudes and Expectations of Teachers and Parents

Social robots are used to perform mobile manipulation tasks, such as tidying up and carrying, based on instructions provided by humans.
A mobile manipulation planner, which is used to exploit the robot's functions, requires a better understanding of the feasible actions in real space based on the robot's subsystem configuration and the object placement in the environment.
This study aims to realize a mobile manipulation planner considering the world state, which consists of the robot state (subsystem configuration and their state) required to exploit the robot's functions.
In this paper, this study proposes a novel environmental representation called a world state-dependent action graph (WDAG).
The WDAG represents the spatial and temporal order of feasible actions based on the world state by adopting the knowledge representation with scene graphs and a recursive multilayered graph structure.
The study also proposes a mobile manipulation planning method using the WDAG.
The planner enables the derivation of many effective action sequences to accomplish the given tasks based on an exhaustive understanding of the spatial and temporal connections of actions.
The effectiveness of the proposed method is evaluated through practical machine experiments performed.
The experimental result demonstrates that the proposed method facilitates the effective utilization of the robot's functions.


A Modular Interface for Controlling Interactive Behaviors of a Humanoid Robot for Socio-Emotional Skills Training

As human–agent collaborations become more prevalent, it is increasingly important for an agent to be able to adapt to their collaborator and explain their own behavior. In order to do so, they need to be able to identify critical states during the interaction that call for proactive clarifications or behavioral adaptations. In this paper, we explore
whether the agent could infer such states from the human's eye gaze for which we compare gaze patterns across different situations in a collaborative task. Our findings show that the human's gaze patterns significantly differ between times at which the user is confused about the task, times at which the agent makes an error, and times of normal workflow. During errors the amount of gaze towards the agent increases, while during confusion the amount towards the environment increases. 
We conclude that these signals could tell the agent what and when to explain.

Analysing Eye Gaze Patterns during Confusion and Errors in Human–Agent Collaborations

In human-robot conversation, the human partner may ask any question within a specific task and expects an answer, ideally alongside a reason for that answer. For example, in a care home for the elderly, a human user might ask a companionship robot, "Where is my mug?", to which the robot would respond, "It is on the counter." For a given interaction to be smooth, a robot should respond correctly and be able to motivate its response, particularly in the cases of ambiguities. When providing answers to a user's question about a given scene, depth plays a critical role as spatial relations within the scene might change with depth as opposed to viewing the scene in 2D. For instance, as shown in Figure 1, when a user asks which object is closest to the white chair, 2D data might be insufficient, but incorporating 3D information can lead to a higher degree of correct responses provided by the robot in response to a human, and thus improve the quality of the interaction.

Improving Visual Question Answering by Leveraging Depth and Adapting Explainability

In the current state-of-the-art, robots are bound to make errors in a human-robot interaction (HRI). Trust is one of the important concepts in HRI that is often lowered by these errors. Fortunately, research has shown there are strategies that can help rebuild trust. An apology made by the robot is one of those strategies. However, apologies can take different forms.
We designed a study in which Nao first built trust with the users, then violated that trust by making a speech recognition error, and then tried to restore it by either an apology with
display of remorse, without remorse, or no apology at all. The results showed expected trends; an apology with remorse in most cases rebuilt trust the strongest. Although the effect of
the type of apology on the trusting beliefs were not significant, the effect on the trusting behaviours was found to be just significant. Suggestions for future research include repeating
the study without its current limitations (more participants, offline) and investigating the accuracy of the portrayed remorse by the robot.

The Robot That Showed Remorse: Repairing Trust with a Genuine Apology

Contemporary approaches to grounded language communication accept an utterance and current world representation as input and produce symbols representing the meaning as output. Since modern approaches to language understanding for human-robot interaction use techniques rooted in machine learning, the quality or sensitivity of the solution is often opaque relative to small changes in input. Although it is possible to sample and visualize solutions over a large space of inputs, naive application of current techniques is often prohibitively expensive for real-time feedback. In this paper we address this problem by reformulating the inference process of Distributed Correspondence Graphs to only recompute subsets of spatially dependent constituent features over a space of sam- pled environment models. We quantitatively evaluate the speed of inference in physical experiments involving a tabletop robot manipulation scenario. We demonstrate the ability to visualize configurations of the environment where symbol grounding produces consistent solutions in real-time and illustrate how these techniques can be used to identify and repair gaps or inaccuracies in training data.

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