Italy

**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://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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IEEE RO-MAN 2022 Conference

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.

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

technical paper

Extending Quantitative Proxemics and Trust to HRI

Exploring requirements and opportunities for social robots in primary mathematics education

Incidental Encounters with Robots

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

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

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

Enhancing learning processes by integrating social robots with learning management systems

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

HAPP: a Haptic Portable Pad for Hand Disease Manual Treatment

A Bidirectional Fabric-based Pneumatic Actuator for the Infant Shoulder: Design and Comparative Kinematic Analysis

https://d3smihljt9218e.cloudfront.net/transcript/40014/subtitle/b4fdaf013d89e46c3c96981016a1bfa7.vtt

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

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Extending Quantitative Proxemics and Trust to HRI

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