Italy

We propose a novel framework for real-time communication-aware coverage control in networked robot swarms. Our framework unifies the robot dynamics with network-level message-routing to reach consensus on swarm formations in the presence of communication uncertainties by leveraging local information. Specifically, we formulate the communication-aware coverage as a cooperative graphical game, and use variational inference to reach mixed strategy Nash equilibria of the stage games. We experimentally validate the proposed approach in a mobile ad-hoc wireless network scenario using teams of aerial vehicles and terrestrial user equipment (UE) operating over a large geographic region of interest. We show that our approach can provide wireless coverage to stationary and mobile UEs under realistic network conditions.

IEEE RO-MAN 2022

CoCo Games: Graphical Game-Theoretic Swarm Control for Communication-Aware Coverage

**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

The impact of COVID-19 has highlighted the
importance of sports and physical activity for maintaining
people’s mental well-being. In this domain, promising use cases
for social robots have been identified. However, current research
lacks a systematic approach for implementing, analyzing and
understanding the contribution of robotic embodiment and
proximity for persuasive and motivation technologies. In this
work, we investigate how social robots could be used, in realworld
training scenarios, to support trainers in conducting
exercises while positively motivating trainees. Two use cases
were designed to meet the needs of health and competitive
oriented trainees. Qualitative and quantitative results from a
field study show positive effects of the robot on trainees in terms
of acceptance and intrinsic motivation. In addition, our findings
highlight directions for further improvement of the design of
social robots in sports and physical scenarios.

Structural optimization of variable stiffness mechanism with particle jamming and core-frame

Accurate detection of the human body and its limbs in
real-time is one of the challenges toward human-robot
collaboration (HRC) technology in the factory of the
future. In this work, a new algorithm has been developed to
fuse thermal, depth, and color information. Starting with
the calibration of the sensors to each other, proceeding
with data fusion and detection of human body parts, and
ending with pose estimation. The proposed approach has been
tested in various HRC scenarios. It has shown a significant
decrease in errors and noise in industrial environments.
Furthermore, it produces not only a higher positioning
accuracy of the head and hands compared to the state of art
algorithms (e.g., OpenPose), but it is also faster. Hence,
such an algorithm could be joined with digital twins for
safe and efficient human-robot collaboration

Use of a Human-Centered Manual Interaction Patterns Analysis Methodology for the Specification of Dexterous Robotic Grippers

The paper addresses aspects of cooperative and uncooperative behaviour in natural language dialogue between humans and social robots. The principles of cooperation in human-human dialogues are taken as the basis for cooperative behaviour in human-robot dialogues. Several approaches are
described that can improve human-robot cooperation. These include more flexible recognition of user intents, more flexible searches using knowledge graphs, generating more cooperative
responses using semantic metadata, and generating more human-friendly responses using Wikipedia. These approaches are demonstrated in a series of videos.

Android Robots Vs Virtual Agents: which system differently aged users prefer?

Movement is one of the basic tools that humans use 
to convey emotional states. Body language and movement are 
also relevant in the perception that we have of other people. 
Many studies have been done on movement and emotion 
sharing, involving humans on one side and robots or automated 
agents on the other. From these studies, there is evidence of the 
importance of robots to improve their social capabilities and
develop more effective social interaction. This work aims at 
embedding some social movements in a robot manipulator, that 
will elicit an emotional response from users. Laban Movement 
Analysis is used to do so, and social movements are developed on 
a robotic arm, then shown to participants in an online study and
a questionnaire. The results show that it is possible to elicit 
emotions through movement only and that the perception is not 
affected by personal experiences.

Second Language Learning through Storytelling with a Social Robot - An Online Case Study

An intersemiotic legal translation involves at least two different semiotic codes, of which at least one is not verbal. The authors will draw a distinction between two macro dimensions of legal translation in digital environments. The first can be schematized in one direction from the machine to the user. Intersemiotic translation is applied in the creation of interfaces (creation of buttons, forms, digital procedures) that are conditions of possibility for the fulfillment of certain legal acts (the acceptance of a user agreement, the purchase of a material or digital asset, the request for a particular document to the PA). The second dimension can be schematized in a direction that goes from the user to the machine. In this case, the user plays the role of the translator since he transposes, through the frontend, his contractual intent from natural language to machine language, using tools available to him by the developer. For example, a team of developers can exploit a Content Management Systems (CMS), which exposes two main interfaces, namely those that connect those who develop the interfaces and those who use them. Respectively, a backend that allows configuring the operating environment (user to machine) and, a frontend that exposes the communication interfaces with the end user (machine to user).

Agents facilitate one category of human empathy through task difficulty

Modern robotics heavily relies on machine learning and has a growing need for training data. Advances and commercialization of virtual reality (VR) present an opportunity to use VR as a tool to gather such data for human-robot interactions. We present the Robot Interaction in VR simulator, which allows human participants to interact with simulated robots and environments in real-time. We are particularly interested in spoken interactions between the human and robot, which can be combined with the robot's sensory data for language grounding. To demonstrate the utility of the simulator, we describe a study which investigates whether a user's head pose can serve as a proxy for gaze in a VR object selection task. Participants were asked to describe a series of known objects, providing approximate labels for the focus of attention. We demonstrate that using a concept of gaze derived from head pose can be used to effectively narrow the set of objects that are the target of participants' attention and linguistic descriptions.

Benefits of an Interactive Robot Character in Immersive Puzzle Games

Existing approaches to direct robot-to-human handovers are typically implemented on fixed-base robot arms, or on mobile manipulators that come to a full stop before performing the handover. We propose "on-the-go" handovers which permit a moving mobile manipulator to hand over an object to a human without stopping. The on-the-go handover motion is generated with a reactive controller that allows simultaneous control of the base and the arm. In a user study, human receivers subjectively assessed on-the-go handovers to be more efficient, predictable, natural, better timed and safer than handovers that implemented a "stop-and-deliver" behavior.

I Can’t Believe That Happened! Exploring Expressivity in Collaborative Storytelling with the Tabletop Robot Ha

Children diagnosed with autism spectrum disorder (ASD) typically work towards acquiring skills to participate in a regular classroom setting, such as attending and appropriately responding to an instructor’s requests. Social robots have the potential to support children with ASD in learning group-interaction skills. However, the majority of studies that target children with ASD’s interactions with social robots have been limited to one-on-one interactions. Group interaction sessions present unique challenges, such as the unpredictable behaviors of the other children participating in the group intervention session and shared attention from the instructor. We present the design of a robot-mediated group interaction intervention for children with ASD to enable them to practice the skills required to participate in a classroom. We also present a study investigating differences in children's learning behaviors during robot-led and human-led group interventions over multiple intervention sessions. Results of this study suggest that children with ASD's learning behaviors are similar during human and robot instruction. Furthermore, preliminary results of this study suggest that a novelty effect was not observed when children interacted with the robot over multiple sessions.

A Social Robot for Anxiety Reduction via Deep Breathing

Learning to recognize new objects in real time in unconstrained environments presents significant challenges for robotic platforms. We present a meta-learning solution to this problem as well as a registered image and events dataset to facilitate work in this domain. Our solution uses interactive motion to isolate the object, and motion-based saliency (from events) to select relevant keypoints from a high-resolution RGB image. Salient keypoints are then passed to a meta-learner to classify the object type. We show that using our interactive isolation and keypoint selection approach, we outperform existing techniques by 6-20%.

Step-by-Step Task Plan Explanations Beyond Causal Links

Explainable robotics refers to the challenge of designing robots that can make their decisions transparent to humans. Recently, a number of approaches to task plan explanation have been proposed, which enable robots to explain each step in their plan to humans. These approaches have in common that they are based on the causal links in the plan. We discuss problems with using causal links for plan explanation. Particularly, their inability to distinguish enabling actions from requiring actions can lead to counter-intuitive explanations. We propose an extension that allows for making this relevant distinction and demonstrate how it can be applied to create a robot that explains its actions.

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