Italy

Supernumerary robotic limbs can act as intelligent prostheses or augment the motion of healthy people to achieve actions which are not possible with only two natural hands. However, as trimanual control is not typical in everyday activities, it is still unknown how different training could influence its acquisition. We conducted an experimental study to evaluate the impact of different forms of trimanual action on training. Two groups of twelve subjects were each trained in virtual reality for five weeks using either a three independent goals task or one dependent goal task. The success of their training was then evaluated by comparing their task performance and motion characteristics between sessions. The results show that subjects dramatically improved their trimanual task performance as a result of training. However, while they showed improved motion efficiency and reduced workload for tasks with multiple independent goals with practice, no such improvement was observed when they trained with the one coordinated goal task.

IEEE RO-MAN 2022

Instructive Interaction for Redirection of Customer Attention from Robot to Service

**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

Collaborative robots that physically interact with humans
in an ergonomic and safe manner are essential to the future
of industry. A common task across many industrial
applications is robot-to-human handover, in which the
location of object exchange is vital in cultivating a
seamless interaction. Most prior work on computing these
exchange locations aims to adjust human posture towards a
better ergonomic state during a single handover. This
procedure typically involves the robot estimating the
human's biomechanical properties, e.g. center of mass and
base of support, before determining an optimal handover
location according to some ergonomics assessment scale. In
a similar vein, we compare two methodologies for object
handover, whereby the handover location is computed to
either "assist" or "stimulate" the human receiver. Unlike
existing approaches, we posit that improvements in human
posture can be derived by stimulating the receiver's
movement dynamics to facilitate posture variability, rather
than constrain or stabilize it. To compare methodologies,
we conduct a within-subjects study where participants
perform 78 object handovers with a collaborative robot
architecture. Our findings indicate an improvement in
ergonomics scores for the "stimulating" approach, hinting
at the importance of productive inconvenience in long-term
robot-to-human handover.

Head Pose for Object Deixis in VR-Based Human-Robot Interaction

For the most comfortable, human-aware robot navigation, subjective user preferences need to be taken into account. This paper presents a novel reinforcement learning framework to train a personalized navigation controller along with an intuitive virtual reality demonstration interface. The conducted user study provides evidence that our personalized approach significantly outperforms classical approaches with more comfortable human-robot experiences. We achieve these results using only a few demonstration trajectories from non-expert users, who predominantly appreciate the intuitive demonstration setup. As we show in the experiments, the learned controller generalizes well to states not covered in the demonstration data, while still reflecting user preferences during navigation. Finally, we transfer the navigation controller without loss in performance to a real robot.

Simple User Assistant Driving Algorithm for Cost Effective Smart Electronic Wheelchair

Wheelchairs are among the most important equipment in terms of facilitating the lives of people with walking disabilities. In particular, the use of electric wheelchairs allows people with walking disabilities to move comfortably without excessively straining their bodies. However, in some cases, a person with a walking disability who uses an electric wheelchair may have difficulties in driving the wheelchair or may experience unintended collisions due to obstacles that are not within the line of sight. In order to prevent such negative events, it is necessary to develop a wheelchair with electric wheels that is produced at a minimum cost, can be easily sold to all kinds of people with disabilities, and will not be difficult to use. In addition, this equipment must include a system designed to provide wheelchair control and driving comfort with maximum efficiency for areas outside of the line of sight, which is one of the features required by all people with walking disabilities. This article explains the design of a prototype that can detect blind spots thanks to sensors and provide driving comfort while a wheelchair driver with walking disabilities uses it. In this paper, it is aimed to create a smart electric wheelchair that can be used by anyone with a walking disability while keeping the costs to a minimum.

On-The-Go Robot-to-Human Handovers with a Mobile Manipulator

A wider incorporation of robots into classrooms is hampered by current technological limitations on full autonomy in social robots. Automated speech recognition, for example, a key enabler for vocal communication, is still unable to perform with sufficient accuracy. Past studies have shown that humans adjust their speech patterns to accommodate less skilled interlocutors. If such a response holds in human-robot interactions as well, we may be able to exploit it to lessen the burden on social robots and enable rich, autonomous vocal communication. 
In this paper we explore whether a robot's speaking ability could have an impact on children's speech patterns, learning, and engagement by designing an interaction where a child and a robot collaborate on a Tower of Hanoi puzzle. Sixteen children aged 7-14 completed this collaborative task partnered with a social robot that communicated with either high verbal (full sentences), low verbal (short phrases or single words), or non-verbal (sound-based utterances) vocalization. While we found no significant impact on children's speech patterns or learning due to the robot's method of vocalization, children in the non-verbal condition had a significantly lower perception of the robot's intelligence along with higher rates of providing feedback and more instances of undoing its moves. This suggests that a link may exist between a robot's perceived speaking ability and children's confidence in that robot's overall intelligence and capability in a collaborative task, as well as their empathy towards a peer they perceive as less skilled in the task.

Recognizing Motion Onset During Robot-assisted Body-weight Unloading is Challenging but Feasible

The eyes play a significant role in how robots are perceived socially by humans due to the eye's centrality in human communication. To date there has been no consistent or reliable system for designing and transferring affective emotional eye gestures to anthropomorphized social robots. Combining research findings from Oculesics, Laban Movement Analysis and the Twelve Principles of Animation, this paper discusses the design and evaluation of the prototype LMA12-O framework for the purpose of maximising the emotive communication potential of eye gestures in anthropomorphized social robots. Results of initial user testings evidenced LMA12-O to be effective in designing affective emotional eye gestures in the test robot with important considerations for future iterations of this framework.

Automatic pathological gait recognition for a mobile robot

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.

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

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.

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Instructive Interaction for Redirection of Customer Attention from Robot to Service

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Head Pose for Object Deixis in VR-Based Human-Robot Interaction

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