Italy

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.

IEEE RO-MAN 2022

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

**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)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[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.

technical paper

Human-robot interaction (HRI) requires quantitative models of proxemics and trust for robots to use in negotiating with people for space. Hall’s theory of proxemics has been used for decades to describe social interaction distances
but has lacked detailed quantitative models and generative explanations to apply to these cases. In the limited case of autonomous vehicle interactions with pedestrians crossing a road, a recent model has explained the quantitative sizes of Hall’s distances to 4% error and their links to the concept of trust in human interactions. The present study extends this model by generalising several of its assumptions to cover further cases including human-human and human-robot interactions.
It tightens the explanations of Hall zones from 4% to 1% error and fits several more recent empirical HRI results. This may help to further unify these disparate fields and quantify them to a level which enables real-world operational HRI applications.

Extending Quantitative Proxemics and Trust to HRI

Novel human-robot collaboration (HRC) methods need careful validation. This is often achieved with user studies in laboratory environments, which mostly rely on highly individual, complex prototype setups in early design stages. Lately, the lack of replicability imposed by such experiments has vitally been discussed. In this paper, we contribute a benchmark toolkit to compose scalable, synthetic tasks as a unified basis for future efforts towards more replicable research. To this end, we propose modular task boards which cover different domains and HRC scenarios. The design of benchmark tasks with these task boards is supported by a software application which generates structural task models and materials for benchmark problem reproduction by 3D printing. Our experiments show that these tasks can be carried out robustly by robots, hence preventing unintended robot failure and providing a controllable, reliably reproducible setting for user studies.

Effects of Robot Clothing on First Impressions, Gender, Human-Likeness, and Suitability of a Robot for Occupations

Collaborative robots have been designed to perform tasks where human cooperation may occur. Additionally, undesired collisions can happen in the robot's environment. A contact classifier may be needed if robot trajectory recalculation is to be activated depending on the source of robot-environment contact. For this reason, we have evaluated a fast contact detection and classification method and we propose necessary modifications and extensions so that it is able to detect a contact in any direction and distinguish if it has been caused by voluntary human cooperation or by accidental collision with a static obstacle for kinesthetic teaching applications. Robot compliance control is used for trajectory following as an active strategy to ensure safety of the robot and its environment. Only sensor data that are conventionally available in commercial collaborative robots, such as joint-torque sensors and joint-position encoders/resolvers,
are used in our method. Moreover, fast contact detection is ensured by using the frequency content of the estimated
external forces, whereas external force direction and sense relative to the robot's motion is used to classify its
source. Our method has been experimentally proven to be successful in a collaborative assembly task for a number of different experimentally recorded trajectories and with the intervention of different operators.

Affective Robot Behavior Improves Learning in a Sorting Game

Fast changing tasks in unpredictable, collaborative environments are typical for medium-small companies, where robotised applications are increasing. Thus, robot programs should be generated in short time with small effort, and the robot able to react dynamically to the environment. To address this we propose a method that combines context awareness and planning to learn Behavior Trees (BTs), a reactive policy representation that is becoming more popular in robotics and has been used successfully in many collaborative scenarios. Context awareness allows for inferring from the demonstration the frames in which actions are executed and to capture relevant aspects of the task, while a planner is used to automatically generate the BT from the sequence of actions from the demonstration. The learned BT is shown to solve non-trivial manipulation tasks where learning the context is fundamental to achieve the goal. Moreover, we collected non-expert demonstrations to study the performances of the algorithm in industrial scenarios.

Fusion of depth, color and thermal images towards safe interaction between human and robot in an industrial environment towards Digital twins

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.

Wizards in the Middle: An Approach to Comparing Humans and Robots

Natural gesturing is very important for the credibility of social robots. It is even more crucial for storytelling robots since the expression, emotion and emphasis must be highlighted. In this paper we propose a hybrid gesture generation approach for a storytelling robot that combines beats automatically generated by a GAN with a probabilistic semantic related gesture insertion system. Beats are executed according to a probability based on the duration of the sentences and semantic gesture insertions are dependent of the previous occurrences of the gestures associated to the words. The polarity of the text is extracted and affects several features of the motion to arouse emotion. A qualitative evaluation of robot behavior is conducted and confirms the approach as a promising one as storytelling system.

“Pepper, what do you mean?” Miscommunication and repair in robot-led survey interaction

Social robots are specialized for social interaction with humans. The promising future and long-term potential of such robots can only be guessed at on the basis of today's pilot applications in many areas. In the field of higher education, only a few attempts have been made to integrate social robots sustainably into educational processes. This research paper presents an approach to integrate the robot model Pepper into educational processes via a learning management system. The goal is to explore and evaluate the perceived benefits and potential of a robot-based learning environment with adaptive educational content from the learning management system (e.g., course content, quiz, survey). An evaluation based on interviews with preservice teachers in business education forms the basis for identifying design implications and for further development. The results of the explorative interview study indicate the relevance of an integration of social robots with learning processes. However, the perceived adaptiveness and educational functionality of the robot within the presented learning environment were limited compared to its positively perceived hedonistic purpose. These findings suggest that technically and pedagogically integrated social robots can be beneficial in facilitating learning, but that their adaptiveness and social capabilities need to be further developed in order to make them effective in this context.

Enhancing learning processes by integrating social robots with learning management systems

The social aspects of therapy and training are
important for patients to avoid social isolation and must be
considered when designing a platform, especially for home-
based rehabilitation. We proposed an online version of the
previously proposed tangible Pacman game for upper limb
training with haptic-enabled tangible Cellulo robots. Our main
objective is to enhance motivation and engagement through
social integration and also to form a gamified multiplayer
rehabilitation at a distance. Thus, allowing relatives, children,
and friends to connect and play with their loved ones while
also helping them with their training from anywhere in the
world. As well as connecting therapists to their patients through
haptically linking capabilities. This is especially relevant when
there are social distancing measures which might isolate the
elderly population, a majority of all rehabilitation patients.

Designing Online Multiplayer Games with Haptically and Virtually Linked Tangible Robots to Enhance Social Interaction in Therapy

Nowadays, especially with the Covid-19 pandemic, researchers are focusing their attention on the remote delivery of devices designed for rehabilitation purposes, allowing people to recover without the physical presence of a doctor.
Manual therapy is a physical treatment that is used by therapists for the treatment of musculoskeletal pain and/or disabilities. 
The aim of this work is to present HAPP, a new haptic portable device, designed to help patients suffering of different patholohgies, as for instance the Complex Regional Pain Syndrome type-1 disease, and more in general to investigate the effects of manual therapy for diseases of the carpus and metacarpus, by mimicking traditional mechanical and rhythmic stimuli characteristics of manual treatments.
Its structure consists of a plate oriented by revolute-prismatic-spherical joints, with a rack-pinion mechanism that actuates the end-effector, stimulating the user's hand palm.
We provide details about the device, such as the mechanical design, the mathematical model and a graphical user interface. Preliminary studies in order to evaluate the device force exerted at the user's palm were carried out.

HAPP: a Haptic Portable Pad for Hand Disease Manual Treatment

As people are getting more used to interact with social robots, their expectations of these robots also increase. However, robots are not always able to meet such expectations due to the limitations of the hardware and the software, or it might be possible that robots are simply unable to correctly elaborate the information about the agents, environment and context, and as consequence they produce erroneous behaviours. For example, a robot might get incongruous responses of people from the observations of multimodal systems. In such case, a technique used by humans is verbal irony, or sarcasm which it is a form of verbal irony, to recover from the situation. To this extent, we present a two parts study where we aimed to endow a robot with sarcasm. Results showed that social interacting behaviours, such as paying attention during a conversation, being transparent on the process of thinking and elaborating a response, allow people's to perceive a robot with higher anthropomorphism and animacy. Moreover, robot failure recovery mechanisms are easier recognised by people when they use verbal incongruence.

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

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Extending Quantitative Proxemics and Trust to HRI

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