Italy

This paper proposes a novel framework for robotic
manipulation tasks, exploiting the Human-Robot
Collaboration (HRC) potential. The framework integrates two
adaptive controllers to i) modulate robot compliance in
contact with the environment along constrained directions,
and to ii) enable human guidance through touch when a
manual intervention is needed. To demonstrate the potential
of the proposed framework, we consider a collaborative
screwing task. In this example application, the operator is
in charge of placing the screws on the table and following
the instructions on a graphical user interface. The robot,
after identifying the position of the screws through an
online human pose-tracking system, performs the screwing
using the proposed controller. The human operator can
adjust the screwing position of the robot using the
adaptive interface at anytime if the position accuracy
through vision is insufficient. We first experimentally
evaluate the operation of the proposed controller and
demonstrate its performance in comparison to the classical
impedance control. Next, the overall system is evaluated in
a collaborative (human and robot) setting.

IEEE RO-MAN 2022

Task sharing with the humanoid robot iCub increases the likelihood of adopting the intentional stance

**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

Nonverbal communication in the field of education can allow teachers to emotionally support their students and improve educational experience and performance. Robot nonverbal movements have been shown to improve both subjective experiences and task performance, and this work investigates whether affective robot behavior can improve human learning. This is tested using an online sorting game where players learn easy or difficult rules, aided by robot feedback videos that contain either neutral or affective movements. Results indicate that affective robot behavior improves learning of the sorting rules and reduces the perceived difficulty of the task. Extensions include expanding the features used to determine the robot feedback and increasing the possible robot motions to create a rich set of robot feedback options to personalize the education experience further for the student.

Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics

Transformative robot sound can improve perceptions of robots, but its implementation will likely require more hardware and cost. Does the addition of transformative sound yield an increase in value to offset this cost? Using the van Westendorp Price Sensitivity Meter, a questionnaire from marketing research, n = 97 participants measured acceptable price points for a robot with (and without) transformative sound. Results showed similar perceptual improvements as past studies, as well as a significant increase in perceived value, when transformative sound was included. These increases in social and value perceptions of robots confirm the utility of adding transformative sound to robots. This work benefits the broader human-robot interaction research community by sharing more ways to understand and validate the incorporation of transformative robot sound and other robot features.

Social Robots as Coaches: How Human-Robot Interaction Positively Impacts Motivation in Sports Training Sessions

In wearable assist devices, the assumed and actual command timings are different. This discrepancy cause problems, such as a decrease in the assist effect. In this study, we focus on viscosity, which is a characteristic of human muscles, and propose viscosity assistance to solve the problems. This method outputs the torque based on the predetermined viscosity coefficient and the actual angular velocity if the torque and angular velocity of the human joints are in opposite directions. The possibility of viscosity assist is demonstrated by analyzing the seated movement, and an assist device using a magnetorheological fluid brake is developed. The assist device is driven using three command inputs: time-based, joint anglebased, and constant viscosity coefficient command inputs. Under conditions based on time and angle, the actual output deviates from the assumed value when the motion is different from the expected motion. In terms of the viscosity command, when only the constant command is used throughout the entire motion section, some subjects showed almost identical results to those expected. Even if a deviation from this assumption is indicated, the degree of agreement can be improved by switching several types of viscosity coefficients during motion.

Toward the manipulation of time and space in extended reality: a preliminary study on multimodal Tau and Kappa illusions in the visual-tactile domain

Social robots are applied in several fields such as education and entertainment. Using the method of storytelling, these two fields can be combined to allow for informal learning. As social robots can benefit students’ motivation and learning process and storytelling is a consolidated method for vocabulary
acquisition, integrating learning content into a robotic storyteller’s narration might facilitate implicit learning. Focusing on language learning, we conducted an online study to compare a
robotic storyteller for vocabulary learning to traditional media, e.g. text. Results show that a robotic storyteller is as effective as traditional media concerning long-term memory. While it is also perceived equally useful, participants were more satisfied when reading text. No differences were indicated for transportation into the story. This preliminary online study indicates the suitability of robotic storytelling for implicit language earning, however, future studies should examine live and co-located scenarios to investigate their full potential.

Designing Social Robots' Speech in the Hotel Context - A Series of Online Studies

Mobile robotic telepresence (MRP) has emerged as a possible solution for supporting health caregivers in a multitude of tasks such as monitoring, pre-diagnosis, and delivery of items. Improved interaction with the system is an important part of using such MRP systems. The current study compared two feedback types ('push' and 'pull') for controlling mobile robots via telepresence. An experimental system that represented a hospital environment was developed. A remote operator (defined as a user) teleoperated a mobile robot to deliver medication supplies to a patient and receive samples from the patient while attending to a secondary task involving medical records. The influence of the feedback types on different aspects of performance and user perception was investigated. User studies were performed with 20 participants coming from two different types of groups – users with and without technological backgrounds. Results revealed that for both user types, the 'push' feedback enhances performance, situation awareness, and satisfaction compared to the 'pull' feedback. The study highlights the potential of improving the telecare experience with MRPs through different feedback types.

Investigating Adaptive Robot Tutoring in a Long-Term Interaction in Higher Education

This paper presents the development and validation process of a robot specific usability questionnaire based on the DIN EN ISO 9241-110:2020-10.
Robotic systems are increasingly prevalent in different work
domains and allow new forms of interaction between robot and
worker. Usability plays an important role for the interaction quality. The IsoMetrics Usability Questionnaire is an established survey instrument for the user-centered usability assessment of interactive systems. It is based on seven principles for the ergonomic design of interactive systems in the DIN EN ISO standard 9241-110. In 2020, a new version of the standard was published,
which changed the norm regarding content and structure. Hence, at this time no robot specific instrument exists that represents the norm accurately.
An initial item pool of 100 robot specific items was assessed in a video-based online study. Exploratory factor analysis showed a ten-factorial structure, closely representing the interaction principles. Confirmatory factor analysis could not replicate these results. To test the construct validity, convergent and di-
vergent constructs were evaluated with mixed results.
The questionnaire in its preliminary form has high content validity, and indicates that the DIN EN ISO standard can be used as a basis for a questionnaire suitable to evaluate usability in robotic systems. However, further research is needed to identify
reasons for the deviating results of the confirmatory factor analysis.

Validation of a usability questionnaire for summative evaluation of robotic systems

The conventional smart gloves present a challenge regarding their portability as most work on gesture recognition techniques based on vision sensing and image processing. The multiple algorithms and signal filtering further make the overall process cumbersome. This work proposes a Shape Memory Alloy (SMA) integrated sensing mechanism in a smart glove for autonomous control. A novel hand gesture recognition technology is developed using kinaesthetic feedback from the finger joint movements. The paper presents a smart glove with an external SMA embedded tubing attachment for the thumb, index, and middle fingers. The motion of the SMA wires is constrained between a fixed end on the tip of the fingers, and the other end is connected to a linear position sensor with spring feedback. The SMA wires in this design exist in their Austenite phase at room temperature, thus exhibiting superelastic or pseudoelastic behavior. The tension in the SMA wire is observed and measured upon bending the fingers, corresponding to the mechanical travel in the linear position sensor. The individual and a combination of position sensor readings are then used as commands for actuating interactive toys. Using a three-finger approach, one can extract seven commands depending upon single or multiple finger movements. This data is further used to actuate the toys, and a use-case for cobotic application is proposed to help better understand interactive play, hand-eye coordination, and thus early cognitive development in children with Autism Spectrum Disorder (ASD). The discrete data output with binary data is independent of other devices or heavy data processing requirements, thus making the proposed novel SM-EXO a better alternative for non-portable and complex smart gloves.

The Impact of an In-Home Co-Located Robotic Coach in Helping People Make Fewer Exercise Mistakes

Many physically demanding tasks require the help of a second person to avoid the otherwise harmful effects of musculoskeletal disorders when performing the task alone. The workforce in many domains, such as nursing care, is rapidly declining. Furthermore, complex repositioning tasks during patient care make it all the more difficult to find a simple solution. Therefore, robotic assistance could be a perfect fit to support this scenario. For this purpose, we conducted a study where 12 caregivers were given the task of repositioning a patient simulator (80 kg) within a nursing bed with the help of a robotic assistant. We measured the physical relief potential using muscle activity and force data while examining this human-robot collaboration problem with two different control approaches, namely a virtual reality-based and a rule-based robot control method. The results show that both methods effectively supported the chosen task with varied strengths and weaknesses. The main advantage of VR-based control is the ability to adapt to the situation and execute quickly (6.61 s), but this also results in higher acting robot forces (35.70 N). With the rule-based control variant, a lower speed of support can be noted on average (10.84 s), but better robot interaction forces (14.95 N) can be discovered in return.

Productive Inconvenience: Facilitating Posture Variability by Stimulating Robot-to-Human Handovers

Educational robotics is a field aiming at investigating the use of robots in schools to support teaching and learning activities. While several robotic solutions exist in support of the STEM teaching activities, in this work, we present ``Classmate Robot" as a new social robot to be used in the classrooms as a support to the learning experience through interaction.
Classmate Robot has been designed and developed to improve the effectiveness of the activities by providing a framework where the robot's behaviors can be personalized, and learning applications can be easily integrated on top of the robot interaction capabilities. This approach aims to increase the engagement of learners. 
We introduce the ROS-based architecture developed that is divided into three main layers plus an application layer. 
As a social robot, it combines several multimodal social cues to interact and communicate with students and teachers. 
Moreover, the robot is endowed with a set of behaviors designed to be compliant with its role of ``classmate" in the interaction with the students.

Towards Subjective Experience Prediction for Time-Delayed Teleoperation with Haptic Data Reduction

Describing pain intensity constitutes an essential part of
pain communication. A medical practitioner cannot depend on
pain scales because of the criteria differences between the
patient and caregiver. However, the context of online pain
communication is dependent on a patient's description and
an assessment on a pain scale. This paper proposes a
robot-assisted pain communication system with a tangible
user interface that enables non-numerical pain
communication. The SQTT interface is proposed using design
processes consisting of a novel squeezable input device and
a twisting robot. The twisting expression of the robot
represents pain intensity, which is gauged from the
squeezing power on the input device. Integrating input and
output requires defining how the twisting motion of the
robot is rendered from the squeezing input. An experiment
was conducted to evaluate four methods of rendering
methods: raw, smoothing (moving average), dynamic
smoothing, and updating peak methods. The result of a
non-parametric one-way analysis of variance indicates a
significant difference between the rendering methods. As a
result, an appropriate rendering method is proposed based
on the ranges: smoothing for mild pain, vibrating for
moderate pain, and exaggerating for severe pain. In
conclusion, The robot-assisted pain communication system
can be implemented with intuitive interaction primitives
for online context. This paper contributes a design lesson
for a robot-assisted online pain communication, which uses
a new method of measuring deformation.

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