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.

In this work, the utilization of Dirichlet (periodic
sinc) base functions in DMPs for encoding periodic motions is
proposed. By utilizing such kernels, we are able to analytically
compute the minimum required number of kernels based only
on the predefined accuracy, which is a hyperparameter that
can be intuitively selected. The computation of the minimum
required number of kernels is based on the frequency content
of the demonstrated motion. The learning procedure essentially
consists of the sampling of the demonstrated trajectory. The
approach is validated through simulations and experiments
with the KUKA LWR4+ robot, which show that utilizing the
automatically calculated number of basis functions, the predefined
accuracy is achieved by the proposed DMP model.

Dirichlet-based Dynamic Movement Primitives for encoding periodic motions with predefined accuracy

technical paper

Virtual Barriers in Augmented Reality for Safe and Effective Human-Robot Cooperation in Manufacturing

A Contact-Adaptive Control Framework for Co-Manipulation Tasks with Application to Collaborative Screwing

Fast Contact Detection and Classification for Kinesthetic Teaching in Robots using only Embedded Sensors

Perceptual Deadband for Haptic Data Compression: Symmetric or Asymmetric?

Explainability in Collaborative Robotics: The Effect of Informing the User on Task Performance and Trust

Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration

Nonverbal cues expressing robot personality - A Movement Analysts Perspective

Multi-perspective human robot interaction through an augmented video interface supported by deep learning

From Message to Expression: Exploring Non-Verbal Communication for Appearance-Constrained Robots

Visualizing Robot Intent for Object Handovers with Augmented Reality

https://d3smihljt9218e.cloudfront.net/transcript/40121/subtitle/6e345e6e267b502014b73558f8569c45.vtt

English (automatic)

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Dirichlet-based Dynamic Movement Primitives for encoding periodic motions with predefined accuracy

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Virtual Barriers in Augmented Reality for Safe and Effective Human-Robot Cooperation in Manufacturing

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