Italy

The advent of collaborative robots has revolutionised the work concept for Small Medium Enterprises (SME), introducing quick line configuration changes and allowing a human operator to work together with a robot. Several problems are still present regarding the need to reprogram the robotic collaborator. This paper exploits the concept of Virtual Reality to instruct the robotic co-worker, allowing a non-skilled operator to teach a task effortlessly and straightforwardly. Each
Skill composing the task is classified and characterised through its pre and postconditions to let the robot stop and notify the operator in case of inability to complete the scheduled action.
Moreover, the system allows the operator to interact with the robot and be proactive during the task execution.

IEEE RO-MAN 2022

A Literature Review of Trust Repair in HRI

**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

In this work, we address two crucial issues that arise in the design of a human-robot collaborative station for the assembly of products: the optimal task allocation and the scheduling problem. We propose an offline method to solve in series the two mentioned issues, considering a static allocation and taking into account several features such as the minimization of postural discomfort, operation processing times, idle times and hence the total cycle time. Our methodology consists of a mixed approach that combines a capability-based method, where the agents’ capabilities are tested against a list of predefined criteria, with optimization. In particular, we formulate a modified version of the Hungarian Algorithm to solve also unbalanced assignment problems, where the number of tasks is different from the number of agents. The scheduling policy is obtained by means of a Mixed Integer Linear Programming (MILP) formulation, with a multi-objective optimization. Moreover, the concepts of operation, assembly tree and precedence graph are formalized, since they represent the inputs to our method, together with the information on the workstation layout and on the selected kind of robot. Finally, the proposed solution is applied to a case study to define the optimal task allocation and scheduling for two different workstation layouts: the results are compared and the best layout is accordingly selected.

Investigating gender-stereotyped interactions with virtual agents in public spaces

Regular exercise provides many mental and physical health benefits. However, when exercises are done incorrectly, it can lead to injuries. Because the COVID-19 pandemic made it challenging to exercise in communal spaces, the growth of virtual fitness programs was accelerated, putting people at risk of sustaining exercise-related injuries as they received little to no feedback on their exercising techniques. Co-located robots could be one potential enhancement to virtual training programs as they can cause higher learning gains, more compliance, and more enjoyment than non-co-located robots. In this study, we compare the effects of a physically present robot by having a person exercise either with a robot (robot condition) or a video of a robot displayed on a tablet (tablet condition). Participants (N=25) had an exercise system in their homes for two weeks. Participants who exercised with the co-located robot made fewer mistakes than those who exercised with the video-displayed robot. Furthermore, participants in the robot condition reported a higher fitness increase and more motivation to exercise than participants in the tablet condition.

Privacy Expectations for Human-Autonomous Vehicle Interactions

Robots operating in public spaces, such as autonomous vehicles, will necessarily collect images and other data concerning the people and vehicles in their vicinity, raising privacy concerns. Common conceptions of privacy in robotics do not include the challenges of many-to-many surveillance where fleets of many individual robots collect data on many people during operation. Technologists, legal scholars, and privacy researchers recommend such technologies fulfill the reasonable privacy expectations of society, but there is no standard method for measuring privacy expectations. We propose a method informed by Contextual Integrity Theory for identifying societal privacy expectations for autonomous vehicle-collected data and codifying the contextual expectations as norms. We present a study (n=600) that identifies twelve distinct norms, which are made up of contextual factors such as the subject of data collection and the data use. In a model for tolerance of autonomous vehicle data collection, we find that both contextual factors related to the data processing and factors related to the individual are significant predictors.

Physical Touch from a Robot Caregiver: Examining Factors that Shape Patient Experience

Collaborative storytelling has long been a goal of social robotics, however, much of this research is limited in interactivity or assumes that story content is curated. In this paper, we present a working fully-automatic collaborative storytelling robot, which can collaborate with a person to create a unique, improvised story by using a large-scale neural language model to dynamically generate continuations to a story. Because effective storytelling requires engaging the emotions of participants, we explore several modalities of procedurally-generated expressivity: 1. an expressive text-to-speech voice with several delivery styles, 2. physical and verbal reactions performed by the robot, and 3. an external display used to show instructions and graphics during storytelling.

To understand the issues associated with improvised collaborative storytelling with a social robot, we conduct an online survey and elicitation study with a group of online observers of collaborative storytelling gameplay, comparing several expressivity strategies in terms of storytelling-related characteristics, expressivity characteristics, and personality traits as measured by RoSAS. This evaluation showed that expressivity strategies using both emotive voice and performed reactions were perceived to be more competent storytellers and more strongly associated with positive personality traits.

Robot-mediated Group Instruction for Children with ASD: A Pilot Study

People often hold inaccurate mental models of robots. When such misconceptions regard a robot's perceptual capabilities, they can lead to issues with safety, privacy, and interaction efficiency. This work is the first attempt to model users' beliefs about a robot's perceptual capabilities and make plans to improve their accuracy---i.e., to perform belief repair. We designed a new domain called the Robot Olympics, implemented it as a web-based game platform for collecting data about users' beliefs, and developed an approach to estimating and influencing users' beliefs about a virtual robot in that domain. We then conducted a study that collected user behavior and belief data from 240 online participants who played the game. Results revealed shortcomings in modeling the participant's interpretations of the robot's actions, as well as the decision making process behind their own actions. The insights from this work provide recommendations for designing further studies and improving user models to support belief repair in human-robot interaction.

Influence of Animallike Affective Non-verbal Behavior on Children’s Perceptions of a Zoomorphic Robot

For a successful deployment of physical Human-Robot Cooperation (pHRC), humans need to be able to teach robots new motor skills quickly. Probabilistic movement primitives (ProMPs) are a promising method to encode a robot’s motor skills learned from human demonstrations in pHRC settings. However, most algorithms to learn ProMPs from human demonstrations operate in batch mode, which is not ideal in pHRC when we want humans and robots to work together from even the first demonstration. In this paper, we propose a new learning algorithm to learn ProMPs incrementally and continuously in pHRC settings. Our algorithm incorporates new demonstrations sequentially as they arrive, allowing humans to observe the robot’s learning progress and incrementally shape the robot’s motor skill. A built-in forgetting factor allows for corrective demonstrations resulting from the human’s learning curve or changes in task constraints. We compare the performance of our algorithm to existing batch ProMP algorithms on reference data generated from a pick-and-place task at our lab. Furthermore, we demonstrate how the forgetting factor allows us to adapt to changes in the task. The incremental learning algorithm presented in this paper has the potential to lead to a more intuitive learning progress and to establish a successful cooperation between human and robot faster than training in batch mode.

Salient Keypoints for Interactive Meta-Learning (SKIML)

This work focuses on the development of a closed-loop controller based on proprioceptive feedback for a pneumatically-actuated soft wearable device aimed at future support of infant reaching tasks. The device consists of two soft pneumatic actuators (one textile-based and one silicone-casted) actively controlling two degrees-of-freedom per arm (shoulder adduction/abduction and elbow flexion/extension, respectively). Inertial measurement sensors attached to the wearable device provide real-time joint angle feedback. Device kinematics analysis is informed by anthropometric data from infants (arm lengths) reported in the literature. Information about range of motion and muscle co-activation patterns in infant reaching is considered to derive desired trajectories for the device's end-effector. Then, a proportional-derivative controller is developed to regulate the pressure inside the actuators and in turn move the arm along the desirable points within the reachable workspace. Experimental results on tracking desired arm trajectories using an engineered mannequin are presented, demonstrating that the proposed controller can help guide the mannequin's wrist to the desired setpoints.

Closed-loop Position Control of a Pediatric Soft Robotic Wearable Device for Upper Extremity Assistance

Studies in Child-Robot Interaction have shown that children form first impressions of a robot's trustworthiness that might influence how they interact with social robots in long-term interactions. However, how children's trust in robots evolves and how it relates to relationship formation is not well understood. This study investigates the effects of repeated encounters with a virtual social robot on children's social and competency trust in social robots and their relationship formation. We developed an online storytelling game with the Furhat robot, where 25 children (9-12 years old) played with the robot over two sessions with seven days of zero exposure in between. Results show that children's competency trust improved with time. We also found empirical evidence that children felt closer to the robot in the second encounter. This work enriches the scientific understanding of children's trust development in social robots over extended periods of time in child-robot collaborative interactions.

Understanding Children's Trust Development through Repeated Interactions with a Virtual Social Robot

Socially Assistive Robots (SARs) show promise to help children during therapeutic and clinical interventions.
However, using SARs for the evaluation of mental wellbeing of children has not yet been explored. Thus, this paper presents an empirical study with 28 children of 8-13 years old interacting with a Nao robot in a 45-minute session where the robot administered (robotised) the Short Mood and Feelings Questionnaire (SMFQ) and the Revised Child Anxiety and Depression Scale (RCADS). Prior to the experimental session, we also evaluated children’s wellbeing using established standardised approaches via online RCADS questionnaires filled by the children (self-report) and their parents (parent-report). We clustered the
participants into three groups (lower, medium, and higher tertile) based on their SMFQ scores. Further, we analysed the questionnaire responses
across the three clusters and across the different modes of administration (self-report, parent-report, and robotised).
Our results show that the robotised evaluation seems to be the most suitable mode in identifying wellbeing related anomalies in children across the three clusters of participants as compared with the self-report and the
parent-report modes. Further, children with decreasing levels of wellbeing (lower, medium and higher tertiles)
exhibit different response patterns: higher tertile are more negative in their responses with the robot while the lower tertile are more positive in their responses with the robot. Findings from this work show that SARs can be used to proactively evaluate mental wellbeing 
related concerns in children.

Can Robots Help in the Evaluation of Mental Wellbeing in Children? An Empirical Study

Previous research has shown that robot mistakes or malfunctions have a significant negative impact on people’s trust. One way to mitigate the negative impact of trust violation is through trust repair. Although trust repair has been studied extensively, it is still not known which strategy is effective in repairing trust in a time-sensitive driving scenario. Additionally, prior research on trust repair has not dealt with the effects of expressing emotion in attempting trust repair. In this paper, we presented the development of a variety of trust repair methods for a time-sensitive scenario using a simulated driving environment as a testbed for validation. These trust repair methods included baseline apology, emotional apology, and explanation. We conducted an experiment to compare the impact of these trust repair methods on human-robot trust. Experimental results indicated that the emotional apology positively affected more participants than the no-repair, baseline apology, and explanation. Furthermore, this study identified emotional apology as the most effective method for the time-sensitive driving scenario.

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A Literature Review of Trust Repair in HRI

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