Italy

IEEE RO-MAN 2022

Towards Robots with Social Resonance: Principles, Challenges and Perspectives

**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.

keynote

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

Parents of children with developmental disabilities face significantly higher workloads then parents of neurotypical children due to their higher care giving demands. Consequently, parents of children with developmental disabilities often face emotional, physical, mental, and social health declines. Currently there has been significant research and development of robots for providing care to children with developmental disabilities to address a variety of care giving scenarios. However, it is presently unclear whether parents would be comfortable with robots interacting with their children in these different child-robot interaction scenarios. In this paper, we investigate parental comfort towards robots caring for children with developmental disabilities in a variety of interaction scenarios and the influence of parental negative attitudes towards robots as well as trust on their comfort towards robots in these scenarios. Overall, our findings suggest that US parental attitudes, trust, and comfort towards robots caring for children with developmental disabilities are neutral. Parents were most comfortable with a robot serving as a teaching assistant to children with a developmental disability and least comfortable as a bus driver. Furthermore, trust for robots had a medium positive association with comfort with child-robot interactions and negative attitudes toward robots had a medium negative association with comfort with child-robot interactions.

Exploring Collaborative Game Play with Robots to Encourage Good Hand Hygiene Practises among Children

While 2D occupancy maps commonly used in mobile robotics
enable safe navigation in indoor environments, in order for
robots to understand and interact with their environment
and its inhabitants representing 3D geometry and semantic
environment information is required. Semantic information
is crucial in effective interpretation of the meanings
humans attribute to different parts of a space, while 3D
geometry is important for safety and high-level
understanding. We propose a pipeline that can generate a
multi-layer representation of indoor environments for
robotic applications. The proposed representation includes
3D metric-semantic layers, a 2D occupancy layer, and an
object instance layer where known objects are replaced with
an approximate model obtained through a novel
model-matching approach. The metric-semantic layer and the
object instance layer are combined to form an augmented
representation of the environment. Experiments show that
the proposed shape matching method outperforms a
state-of-the-art deep learning method when tasked to
complete unseen parts of objects in the scene. The pipeline
performance translates well from simulation to real world
as shown by F1-score analysis, with semantic segmentation
accuracy using Mask R-CNN acting as the major bottleneck.
Finally, we also demonstrate on a real robotic platform how
the multi-layer map can be used to improve navigation
safety.

Designing Long-term Parent-child-robot Triadic Interaction at Home through Lived Technology Experiences and Interviews

Robot personality design is an essential aspect in Social Robotics, where humans and robots interact in a social context. Social robots with expressive behaviors can demonstrate human-like behaviors through voice, speech, gestures, and non-verbal cues. This study focuses on
designing robot personalities based on non-verbal cues. More specifically, we investigate how human experts in
movement analysis perceive different personalities of robots (introvert vs. extrovert) based on the robot’s
movement and other dynamic features, e.g., the position of joints, head and torso, voice pitch, speed, etc. We present our results from a thematic analysis of the data collected during the focus group with the movement analysis
experts who observed designed extrovert and introvert robot personalities performed by a Pepper robot. Our findings result in guidelines on designing different robot movement
features, emphasizing body symmetry, consistency and congruence during the interaction emphasized by the
movement analysts. These are not found in the existing literature. Based on the focus group findings and
literature study, we summarise the design guidelines for extrovert and introvert robot behaviors in a social
context.

Exploring requirements and opportunities for social robots in primary mathematics education

Could a social robot use force to prevent violence directed toward humans in its care? -Might crime be eradicated, or conversely could excessive use of force proliferate and human dignity become trampled beneath cold robotic wheels?
Such speculation is one part of a larger, increasingly important question of how social robots will be expected to behave in our societies, as robotic technologies develop and become increasingly widespread. Here, to gain some insight into this topic of "robot self-defense", we proposed a simplified heuristic based on perceived risk of loss to predict acceptability, and conducted a user survey with 304 participants, who watched eight animated videos of robots and humans in a violent altercation. The results indicated that people largely accept the idea that a humanoid robot can use force on attackers to help others. Furthermore, self-defense was perceived as more acceptable when the appearance of the defender was humanoid rather than mechanical, and when the force disparity between attacker and defender was high.
The immediate suggestion is that it could be beneficial to re-examine common assumptions that a robot should never harm or risk harming humans, and to discuss and consider the possibilities for robot self-defense.

Incidental Encounters with Robots

Dense crowds are challenging scenes for an autonomous mobile robot. Planning in such an interactive environment requires predicting uncertain human intentions and
reactions to future robot actions. Concerning these capabilities,
we propose a probabilistic forecasting model which factorizes
the human motion uncertainty as follows: 1) A (conditioned)
normalizing flow (CNF) estimates the densities of human goals.
2) The density of trajectories toward goals is predicted autoregressively (AR), where the density of individual social actions
is inferred simultaneously for a dynamic number of humans.
The underlying Gaussian AR framework is extended with our
SocialSampling to counteract collisions during sampling. The
model allows us to determine a crowd prediction conditional
on a particular robot plan and a crowd prediction independent
of it for the same goals. We demonstrate that the divergence
between the two probabilistic predictions can be efficiently
determined and we derive our Social Influence (SI) objective
from it. Finally, a model-predictive policy for robot crowd
navigation is proposed that minimizes the SI objective. Thus,
the robot reflects its future movement in order not to disturb
humans in their movement if possible. The experiments on
real datasets show that the model achieves state-of-the-art
accuracy in predicting pedestrian movements. Furthermore,
our evaluations show that robot policy with our SI objective
produces safe and proactive behaviors, such as taking evasive
action at the right time to avoid conflicts.

Towards understanding causality – a retrospective study of scenarios that trigger explanations in interactions between a humanoid robot and autistic children

In the well-known Turing test, humans have to judge whether they write to another human or a chatbot. In this article, we propose a reversed Turing test adapted to live conversations: based on the speech of the human, we have developed a model that automatically detects whether she/he speaks to an artificial agent or a human. We propose in this work a prediction methodology combining a step of specific features extraction from behaviour and a specific deep learning model based on recurrent neural networks. The prediction results show that our approach, and more particularly the considered features, improves significantly the predictions compared to the traditional approach in the field of automatic speech recognition systems, which is based on spectral features, such as Mel-frequency Cepstral Coefficients (MFCCs). Our approach allows evaluating automatically the type of conversational agent, human or artificial agent, solely based on the speech of the human interlocutor. Most importantly, this model provides a novel and very promising approach to weigh the importance of the behaviour cues used to make correctly recognize the nature of the interlocutor, in other words, what aspects of the human behaviour adapts to the nature of its interlocutor.

Towards Robots with Social Resonance: Principles, Challenges and Perspectives

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