Italy

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.

IEEE RO-MAN 2022

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

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

technical paper

**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/)

Registration Is Required

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.

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

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.

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.

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Extending Quantitative Proxemics and Trust to HRI

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