Italy

Managing robot workloads in human robot teams is critical for efficient team operation. If robots are overloaded with work, then they will miss deadlines and force humans to take on extra work. This paper presents a framework for a robot to assess its own workload based on an initial goal assignment. The robot does this by generating task and motion plans and computing the probability of missing deadlines due to the possibility of delays in task execution. A branch and bound based search is used to generate task and motion plans by minimizing task execution effort. The robot presents a diverse set of task and motion plans to the humans to offer multiple different options. Humans can either approve a plan or provide guidance to reduce the workload by either relaxing deadlines or removing goal(s) assigned to the robots.

IEEE RO-MAN 2022

Who’s on First? The Impact of the Proactive Interaction on User Acceptance of the Robotized Object

**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)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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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

Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention---choice with commitment---following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I argue that though models of machine Theory of Mind need not follow the same learning progression as children, intention is an aspect of Theory of Mind that should be more explicit.

AI bias in Human-Robot Interaction: An evaluation of the Risk in Gender Biased Robots

Social robots have been introduced in different fields such as retail, health care and education. Primary education in the Netherlands (and elsewhere) recently faced new challenges because of the COVID-19 pandemic, lockdowns and quarantines including students falling behind and teachers burdened with high workloads. Together with two Dutch municipalities and nine primary schools we are exploring the long-term use of social robots to study how social robots might support teachers in primary education, with a focus on mathematics education. This paper presents an explorative study to define requirements for a social robot math tutor.
Multiple focus groups were held with the two main stakeholders, namely teachers and students. During the focus groups the aim was 1) to understand the current situation of mathematics
education in the upper primary school level, 2) to identify the problems that teachers and students encounter in mathematics education, and 3) to identify opportunities for deploying a social robot math tutor in primary education from the perspective of both the teachers and students. The results inform the development of social robots and opportunities for pedagogical methods used in math teaching, child-robot interaction and potential support for teachers in the classroom.

Don't Get into Trouble! Risk-aware Decision-Making for Autonomous Vehicles

This study explored the culture and gender differences among older adults interacting with polite non-humanoid robots. We have used Lakoff theory for the polite conversational maxims. A within-subjects experiment was designed with polite and non-polite, correct and erroneous behaving robots. The polite robot employs three sub-rules (“don’t impose,” “give options,” and “be friendly”). A user study was conducted with older adults from two cultural backgrounds: Israeli Jewish and Arab participants. The study revealed that the participants could not differentiate between the polite behaviors when the robot was correct. They were more annoyed with a polite robot making an error as compared to a non-polite erroneous robot. Whereas gender had no impact on participants’ evaluations, there were significant cultural differences between the groups: Although the Arab participants were primarily more skeptical, they enjoyed, were satisfied, and trusted the robot more than the Jewish participants. In addition, they preferred the direct non-polite behavior, whereas the Jewish participants liked the polite behavior more.

A Non-Humanoid Robotic Object for Providing a Sense Of Security

Having a sense of security is considered a basic
human emotional need. It increases confidence, encourages exploration,
and enhances relationships with others. In this study
we tested the possibility of leveraging the interaction with a
simple non-humanoid robot for increasing participants’ sense of
security. The robotic behavior was designed with a psychology
expert in attachment theory and was translated into the robot’s
morphology by an animator. Specifically, the robot was designed
to be attentive and responsive using lean, gaze and nodding
gestures. We compared participants’ experience in the secure
condition to the experience of participants who interacted with
a non-responsive robot. We further compared the participants’
implicit sense of security between the robotic conditions and
an additional baseline condition in which participants did not
interact with the robot. Our findings indicate the potential in
leveraging a simple non-humanoid robot for enhancing humans’
sense of security.

Robot Self-defense: Robots Can Use Force on Human Attackers to Defend Victims

Gaze cues play an important role in human communication and are used to coordinate turn-taking and joint attention, as well as to regulate intimacy. In order to have fluent conversations with people, social robots need to exhibit humanlike gaze behavior. Previous Gaze Control Systems (GCS) in HRI have automated robot gaze using data-driven or heuristic approaches. However, these systems tend to be mainly reactive in nature. Planning the robot gaze ahead of time could help in achieving more realistic gaze behavior and better eye-head coordination. In this paper, we propose and implement a novel planning-based GCS. We evaluate our system in a comparative within-subjects user study (N=26) between a reactive system and our proposed system. The results show that the users preferred the proposed system and that it was significantly more interpretable and better at regulating intimacy.

Eye Design of Social Robots Inspired by the Difference of Gaze Clarity in Canid Species

The supervision component is the binder of a robotic architecture. Without it, there is no task, no interaction happening, it conducts the other components of the
architecture towards the achievement of a goal, which means, in the context of a collaboration with a human, to bring changes in the physical environment and to update the human partner mental state. However, not so much work focus
on this component in charge of the robot decision-making and control, whereas this is the robot puppeteer. Most often, either tasks are simply scripted, or the supervisor
is built for a specific task. Thus, we propose JAHRVIS, a Joint Action-based Human-aware supeRVISor. It aims at being
task-independent while implementing a set of key joint action and collaboration mechanisms. With this contribution, we intend to move the deployment of autonomous collaborative robots forward, accompanying this paper with our open-source code.

Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

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.

A Literature Review of Trust Repair in HRI

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

Due to advances in technology, various types of existing products can be robotized. The interaction design between the robotized objects which are the robotized products and users has become an emerging issue since how to interact affects user acceptance of the new products. Because the robots can perceive, recognize and assist the users, robotized objects can assist users by reading the context and guessing their intentions in advance, or can assist users in response to users’ clear requests. In order to explore the effective interaction strategy of robotized object, we conduct 2 × 2 mixed-participant experiment. As a result, participants were more surprised by and felt positive emotion toward robotized objects with proactive interaction than with reactive interaction. In addition, the robotized object with proactive interaction was evaluated as more intelligent and easier to use than that with reactive interaction. The influence of interaction strategies on satisfaction of robotized objects was mediated by surprise and positive emotions. Furthermore, the robotized object with proactive interaction was evaluated as more positive when the user’s intention was expressed only by action cue than when it was expressed by both verbal cue and action cue.

Timing-Specified Controllers with Feedback for Human-Robot Handovers

Vision plays an important role in motion planning for mobile robots which coexist with humans. Because a method predicting a pedestrian path with a camera has a trade-off relationship between the calculation speed and accuracy, such a path prediction method is not good at instantaneously detecting multiple people at a distance. In this study, we thus present a method with visual recognition and prediction of transition of human action states to assess the risk of collision for selecting the avoidance target. The proposed system calculates the risk assessment score based on recognition of human body direction, human walking patterns with an object, and face orientation as well as prediction of transition of human action states. First, we investigated the validation of each recognition model, and we confirmed that the proposed system can recognize and predict human actions with high accuracy ahead of 3 m. Then, we compared the risk assessment score with video interviews to ask a human whom a mobile robot should pay attention to, and we found that the proposed system could capture the features of human states that people pay attention to when avoiding collision with other people from vision.

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Who’s on First? The Impact of the Proactive Interaction on User Acceptance of the Robotized Object

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AI bias in Human-Robot Interaction: An evaluation of the Risk in Gender Biased Robots

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