Italy

In this work we present a novel attention deep learning model that uses context and human intention for 3D human body motion prediction in handover human-robot tasks. This model uses a multi-head attention architecture which incorporates as inputs the human motion, the robot end effector and the position of the obstacles. The outputs of the model are the predicted motion of the human body and the predicted human intention. We use this model to analyze a handover collaborative task with a robot where the robot is able to predict the future motion of the human and use this information in it&#39;s planner. Several experiments are performed where human volunteers fill a standard poll to rate different features, taking into account when the robot uses the prediction versus when the robot doesn&#39;t use the prediction.

IEEE RO-MAN 2022

Context and Intention for 3D Human Motion Prediction: Experimentation and User study in Handover Tasks

**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.
 
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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.

In this work we present a novel attention deep learning model that uses context and human intention for 3D human body motion prediction in handover human-robot tasks. This model uses a multi-head attention architecture which incorporates as inputs the human motion, the robot end effector and the position of the obstacles. The outputs of the model are the predicted motion of the human body and the predicted human intention. We use this model to analyze a handover collaborative task with a robot where the robot is able to predict the future motion of the human and use this information in it's planner. Several experiments are performed where human volunteers fill a standard poll to rate different features, taking into account when the robot uses the prediction versus when the robot doesn't use the prediction.

technical paper

The role of social robots as advisors for decision
making is investigated. It has been consistently shown that when
asked to rank options, people often make fallacious judgements.
Furthermore, such fallacies can be sensitive to presentation
mode.We study whether having social robot advisors presenting
options can mitigate and reduce the fallacy rates of participants.
For this purpose we explored a novel presentation mode of
options with conjunction judgmental fallacy, namely, choosing
among different rank-orders, as opposed to rank the options
themselves.We first show that the mere presentation mode has a
significant mitigating effect on the fallacy rates.We then further
show that when social robot advisors present the rank-orders,
the fallacy rates of participants significantly decrease even
further. Moreover, participants perceive the fallacious robot as
more likeable and intelligent, but assign the non-fallacious robot
to trustworthy roles, such as jury and analyst. These results
suggest that social robot advisors may be used to influence and
mitigate human fallacious judgmental decision making.

Mitigating Judgmental Fallacies with Social Robot Advisors

When acting together with another agent, humans form shared representations with their partner, in order to predict and adjust to their partner’s behaviors and ensure a smooth and efficient joint action. Previous work has shown that humans do not form shared representations when acting with partners that are not perceived as intentional, such as computers or robots. In the current study, we investigated whether engaging in a complementary joint action task with the humanoid robot iCub could influence the perceived intentionality of the robot. Participants completed a target tracking and detection task with iCub, in which they had to act together with the robot to achieve a shared goal. Participants’ likelihood of perceiving the robot as intentional was assessed before and after the interaction. Results showed that participants were more likely to perceive iCub as an intentional agent after they shared a task with it, suggesting that interacting with a robot to achieve a common goal promotes intentionality attribution toward the robot.

A Benchmark Toolkit for Collaborative Human-Robot Interaction

Autonomous vehicles have an existential communication challenge due to the lack of need for a human driver who can signal to vulnerable road users nearby about the intentions of the vehicle. This presents an opportunity for a vehicle to vulnerable road user communication system, such as for bicyclists. Enabling communication between bicyclists and autonomous vehicles will lead to an improvement of the bicyclists' safety in autonomous driving. If a bicyclist wants to pass the autonomous vehicle, the autonomous vehicle should provide feedback to the human about what it is about to do and what it would like the person to do. The user study presented in this paper investigated several possible options for an external display for effective nonverbal communication between an autonomous vehicle and a bicyclist. 
The results were compared to our recent study concerning vehicle-to-pedestrian communication. In total 208 participants were recruited for the vehicle-to-walker and vehicle-to-bicyclist feedback module studies.
The results did not show significant differences between the communication modalities presented. This paper shows and discusses differences between vehicle-to-walker and vehicle-to-bicyclist feedback modules. It is plausible to use the same combination of interaction modes, symbols and text, as for the vehicle-to-pedestrian communication feedback module due to economic reasons.
This study shows the necessity for more immersive environments to study vehicle to bicyclist communication needs in more detail.

Measuring Visual Social Engagement from Proxemics and Gaze

Humor usually plays a positive role in social activities. We posit that endowing a social robot with humor ability can enhance expressive human-robot interaction. People's perception on humor is different, and therefore, making the robot express humor in an appropriate way is a challenge. The main aim of this paper is to explore the correlation between people's perception on different types of jokes and their humor styles (Affiliative, Self-enhancing, Self-defeating, Aggressive). In the experiment, we used the humanoid robot Pepper to perform different types of jokes. Both subjective (jokes rating) and objective measures (RGB and thermal images) were used. The latter method was employed to extract facial features (facial action unit and temperature). After extracting and analyzing the data of both measurement methods, we found that the Self-defeating humor style positively affects people's rating on all types of jokes. In addition, there is also a positive correlation between people's humor style scores and the degree of happiness.

Why do you think this joke told by robot is funny? The humor style matters

Human motion trajectory prediction, an essential task for autonomous systems in many domains, has been on the rise in recent years. With a multitude of new methods proposed by different communities, the lack of standardized benchmarks and objective comparisons is increasingly becoming a major limitation to assess progress and guide further research. Existing benchmarks are limited in their scope and flexibility to conduct relevant experiments and to account for contextual cues of agents and environments. In this paper we present Atlas, a benchmark to systematically evaluate human motion trajectory prediction algorithms in a unified framework. Atlas offers data preprocessing functions, hyperparameter optimization, comes with popular datasets and has the flexibility to setup and conduct underexplored yet relevant experiments to analyze a method’s accuracy and robustness. In an example application of Atlas, we compare five popular model- and learning-based predictors and find that, when properly applied, early physics-based approaches are still remarkably competitive. Such results confirm the necessity of benchmarks like Atlas.

The Atlas Benchmark: an Automated Evaluation Framework for Human Motion Prediction

This work proposes EvaSIM (EVA Robot
Simulator), a simulator that can
interpret an EVA script code and
emulate the multimodal interaction
capabilities of the physical robot, such
as Text-To-Speech, facial expression
recognition, controlling light sensory
effects, etc. EVA robot is an open-
source robotics platform intended to
serve as a tool to assist in HRI research.

EvaSIM: a Software Simulator for the EVA Open-source Robotics Platform

Adolescents isolated at home during the COVID-19 pandemic lockdown are more likely to feel lonely and in need of social connection. Social robots may provide a much needed social interaction without the risk of contracting an infection. In this paper, we detail our co-design process used to engage adolescents in the design of a social robot prototype intended to broadly support their mental health. Data gathered from our four week design study of nine remote sessions and interviews with 16 adolescents suggested the following design requirements for a home robot: (1) be able to enact a set of roles including a coach, companion, and confidant; (2) amplify human-to-human connection by supporting peer relationships; (3) account for data privacy and device ownership. Design materials are available in open-access, contributing to best practices for the field of Human-Robot Interaction.

Robots for Connection: A Co-Design Study with Adolescents

Emotional expressivity is essential for successful
Human-Robot Interaction. However, robots often have different
levels of expressivity in their face and voice. Here we ask
whether this modality mismatch influences human behaviour
and perception of the robot. Participants played a cooperative
task with a robot that displayed matched and mismatched
smiling expressions in the face and voice. Emotional expressivity
did not influence acceptance of robot’s recommendations or
subjective evaluations of the robot. However, we found that
the robot had overall a higher social influence than a virtual
character, and was evaluated more positively.

Usability of an Immersive Control System for a Humanoid Robot Surrogate

Individuals living with cognitive impairments are faced with unique challenges in completing important activities of daily living such as dressing. In this paper, we present the first socially assistive robot-wearable sensors system to provide dressing assistance through social human-robot interactions. A novel robot-wearable architecture is development to classify, prompt and provide feedback on user dressing actions. Namely, strain sensor based smart clothing on the user are used for joint angle mapping, which are then classified into different dressing steps. The robot uses a MAXQ hierarchical learning method to learn assistive behaviors to aid a user with the sequence of dressing steps. Experiments were validated the performance of the joint angle mapping model, dressing action classifier, and behavior adaptation modules as well as the overall system for dressing assistance.

Self-Disclosure to a Robot “In-the-Wild”: Category, Human Personality and Robot Identity

A psycho-physical experiment using constant method was conducted to investigate the characteristics of pseudo force sensation caused by pressurization to a palm. Kinesthetic force stimulus and tactile pressure stimulus were given to a subject at the same time, and their effect on force perception was investigated. Previous studies have reported two contradicting results regarding whether the strength of pseudo force sensation depends on the pressure or on the total amount of force. This paper further investigated this aspect using three types of indenters which differ in shape and width. The results suggested that the pseudo force sensation does not depend on total force. This, however, does not simply mean that the sensation depends on the pressure and the need for further investigations remains. The results found significant differences among subjects but also suggested that the narrower indenter with an integrated body is less susceptible to individual differences among subjects.

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Context and Intention for 3D Human Motion Prediction: Experimentation and User study in Handover Tasks

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Mitigating Judgmental Fallacies with Social Robot Advisors

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