Italy

The gesture is one of the most used forms of communication between humans; in recent years, given the new trend of factories being adapted to the Industry 4.0 paradigm, the scientific community has shown a growing interest in the design of Gesture Recognition (GR) algorithms for Human-Robot Interaction (HRI) applications.
Within this context, the GR algorithm needs to work in real-time and over embedded platforms, with limited resources. Anyway, when looking at the available scientific literature, the aim of the different proposed neural networks (i.e. 2D and 3D) and of the different modalities used for feeding the network (i.e. RGB, RGB-D, optical flow) is typically the optimization of the accuracy, without strongly paying attention to the feasibility over low power hardware devices. 
Anyway, the analysis related to the trade-off between accuracy and computational burden (for both networks and modalities) becomes essential so as to allow GR algorithms to work in industrial robotics applications. 
In this paper, we perform a wide benchmarking focusing not only on the accuracy but also on the computational burden, involving two different architectures (2D and 3D), with two different backbones (MobileNet, ResNeXt) and four types of input modalities (RGB, Depth, Optical Flow, Motion History Image) and their combinations.

IEEE RO-MAN 2022

Exploring First Impressions of the Perceived Social Intelligence and Construal Level of Robots that Disclose their Ability to Deceive

**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)

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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

Zoomorphic robots are a promising tool for animal welfare education and could be used to teach children that animals have minds and emotions and thereby reduce acceptance of cruelty towards animals. This study investigated the influence of animallike affective non-verbal behavior on children's perceptions of the attributes and mental abilities of a zoomorphic robot, as well as their acceptance of cruelty towards it. Children who interacted with a robot that displayed animallike affective non-verbal behavior ascribed a significantly higher level of mental abilities. Higher levels of perceived mental abilities were not generally correlated with lower acceptance of cruelty but higher levels of perceived social attributes were. Post-hoc analysis of reasoning given for unacceptability of cruelty found that the group of children who made moral judgments about the cruelty had rated the zoomorphic robot as significantly more animate.

The Robot Screener Will See You Now: A Socially Assistive Robot for COVID-19 Screening in Long-Term Care Homes

Recurrent or sustained awkward body postures are among the most frequently cited risk factors to the development of work-related musculoskeletal disorders (MSDs). To prevent workers from adopting harmful configurations but also to guide them toward more ergonomic ones, wearable haptic devices may be the ideal solution. In this paper, a vibrotactile unit, called ErgoTac, and a slide-and-squeeze unit, called CUFF, were evaluated in a limbs postural correction setting. Their capability of providing single-joint (shoulder or knee) and multi-joint (shoulder and knee at once) guidance was compared in twelve healthy subjects, using quantitative task-related metrics and subjective quantitative evaluation. An integrated environment was also built to ease communication and data sharing between the involved sensor and feedback systems. Results show good acceptability and intuitiveness for both devices. ErgoTac appeared as the suitable feedback device for the shoulder, while the CUFF may be the effective solution for the knee. This comparative study, although preliminary, was propaedeutic to the potential integration of the two devices for effective whole-body postural corrections, with the aim to develop a feedback and assistive apparatus to increase workers' awareness about risky working conditions and therefore to prevent MSDs.

Performance Analysis of Vibrotactile and Slide-and-Squeeze Haptic Feedback Devices for Whole-Body Postural Adjustment

Cognitive training is effective at retaining cognitive function and delaying decline for typically ageing older adults, individuals with mild cognitive impairment, and persons with dementia. Technological resources can address limiting factors that inhibit engagement and access to this treatment. We investigated how a socially assistive robot-facilitated memory task with sensory feedback was received by older adults. The impact of unimodal and multimodal administration of auditory and haptic feedback using two robot embodiments (Pepper and Nao) was evaluated in terms of user performance, usability, and workload. In contrast to sensory feedback research, auditory feedback resulted in significantly higher task accuracy. This was, however, supported by previous work from neurological literature. Auditory feedback also received significantly higher usability, and this preference was validated by qualitative feedback from participants. Regardless of robotic embodiment, this study demonstrates an advantage for auditory feedback (over haptic and multimodal) in cognitive training activities for older adults.

Investigating the Usability of a Socially Assistive Robotic Cognitive Training Task with Augmented Sensory Feedback Modalities for Older Adults

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.

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

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.

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