Italy

In music education, staying motivated has always been a crucial goal, especially for children. Earlier research has demonstrated how social robots could help improve motivation and increase the performance of children practicing music pieces. As important stakeholders, the attitudes and expectations of parents and music teachers are important factors that affect children’s and their own acceptance of applying social robots in music education. In this study, a survey was created and used to assess parents’ and music teachers’ attitudes, expectations, and intentions related to using social robots in music education. The survey results suggested that parents and music teachers expect robots to act as assistive and motivating agents, give positive feedback, and schedule and structure practice sessions. Moreover, parents expect a social robot to evaluate student performance, while music teachers focus more on positive encouragement. The biggest concerns of these stakeholders are whether the robots can convey the emotional aspects of music and demonstrate how to play the instrument. The intention of using robots is positively correlated with positive attitudes toward robots in general and the positive attitude toward robots specifically in the context of music education. The current study contributes insights into parents’ and music teachers’ attitudes and expectations as determinants of their acceptance of social robots in music education and provides a method for measuring these attitudes and expectations.

IEEE RO-MAN 2022

Activity Recognition in Restaurants to Address Underlying Needs: A Case Study

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year! 

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).

CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

technical paper

**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/)

Registration Is Required

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.

In Minimal Invasive Surgical procedures, flexible bevel tip needles are widely used for percutaneous intervention
due to the advantage of enhancing the target reaching accuracy. Here, the target reaching accuracy suffers due to tissue in-homogeneity, deformation in tissue domain and improper steering techniques. The main objective of the percutaneous intervention procedure is ensuring patient safety and reaching
desired target position accurately. Several researchers have already developed many approaches to control the needle steering for precise target reaching. To overcome complex
approaches in existing controllers, we have proposed a fuzzy based controller to regulate the needle in a specified plane. Our designed method involves the needle non-holonomic constraints
based kinematics inside tissue domain and Lyapunov analysis based fuzzy rule base for fuzzy inference system which ensures the closed loop stability of needling system for percutaneous
intervention procedure. We have also validated our designed control scheme through extensive simulations and experimentations in biological tissue.


Using the Price Sensitivity Meter to Measure the Value of Transformative Robot Sound

When navigating in a shared environment, the extent to which robots are able to effectively use signals for coordinating with human behaviors can ameliorate dissatisfaction and increase acceptance. In this paper, we present an online video study to investigate whether familiar acoustic signals can improve the legibility of a robot's navigation behavior. We collected the responses of 120 participants to evaluate their perceptions of a robot that communicates with one of the three used non-verbal navigational cues (an acoustic signal, an acoustic in pair with a visual signal, and a dissimilar frequency acoustic signal). Our results showed a significant legibility improvement when the robot used both light and acoustic signals to communicate its intentions compared to using only the same acoustic sound. Additionally, our findings highlighted that people also perceived differently the robot's intentions when they were expressed through two frequencies of the mere sound. The results of this work suggest a paradigm that can help the development of mobile service robots in public spaces.

Two dimensions of the intersemiotic legal translation in digital environments. Programming legal acts as a resemiotization process.

Robotic comedians (and social robots generally) need to recognize and adapt to human responses during playful
dialog. To support this ability, we determined design guidelines
via a survey of 20 human comedians and developed a machine learning pipeline to support comedian-like behaviors by our robotic system. Based on comedian input, we identified that discerning laughter vs. no laughter during a joke setup and big laugh vs. so-so response vs. no laugh after a punchline were important skills for a comedian. To enable these abilities in a robotic system, we used an existing dataset of robot comedy performance audio to train classifiers for audience
responses during the setup and after the punchline of jokes. Top-performing models for the above types of discernment performed similarly to human raters who completed the same
classification task. Comparison of the current results to our past efforts of a similar nature reveal repeatability of top-performing approaches and generalizability of the approaches to new parts of robot comedy routines. The social intelligence supported by this work can promote the likability and acceptance of robots.

How long does it take to learn trimanipulation?

Face spoofing detection could be used in conjunction with face recognition or facial expression recognition in social robots for better interaction with the users. Recent works have employed thermal image-based spoofing detection due to its competitive spoofing detection performance. However, existing methods utilized indoor thermal images which put a limit on using thermal image-based spoofing detection in outdoor environments. Moreover, several thermal image-based spoofing detection datasets consist of facial images at a frontal viewpoint, which would be put a limit on the usage in social robots with short heights. In this paper, we present our novel thermal image dataset for spoofing detection in outdoor environments. Unlike most existing datasets, our dataset is collected in outdoor environments with the camera facing the upper view to simulate the viewpoint of a social robot. To cover diverse outdoor environments, the dataset was collected at varying locations, times, and weather. Furthermore, we trained an event recognition-based spoofing detection on our collected dataset and also have provided benchmarking results for several baselines. Our spoofing detection method based on MobileNetV2 yielded a spoofing detection accuracy of 96.04\% and GFLOPs of 0.88. Our dataset will be open-sourced for users pursuing academic research and will be available upon request to the authors.

Towards evaluating the impact of swarm robotic control strategy on operators' cognitive load

Robots are becoming increasingly prominent in the entertainment sphere, where they interact with guests in themed environments to tell stories, often in place of human characters. To evaluate the potential benefits of robots in these contexts compared to humans, we created an interactive puzzle game where either a robot or a human actor serves as a diegetic "game guide" character that is both a cooperative partner and an omniscient game master. In the game, participants solve a crime mystery by asking the game guide for information to complete tasks and for hints to solve puzzles. We conducted a between-subjects study (n = 42) to investigate how players' game experiences differed when the game guide was a human compared to an embodied robot. Our results show that participants playing with a robot had more fun, felt less judged, and felt more connected with the robot while solving tasks compared to those playing with a human. These results suggest that robots can be effective alternatives to human actors in broader immersive entertainment contexts such as escape rooms to provide greater enjoyment and promote more social interaction with in-game characters.

Talk to Kotaro: a web crowdsourcing study on the impact of phoneme and prosody choice for synthesized speech on human impression

The use of multi-robot systems is increasing in
disaster response, industry, transport, and logistics. Humans
will remain indispensable to control and manage these fleets
of robots, particularly in safety-critical applications. However,
a human operator’s cognitive capacities can be challenged and
exceeded as the sizes of autonomous fleets grow, and more
sophisticated AI techniques can lead to opaque robot control
programs. In a user study (n = 40), we explore how autonomous
swarm intelligence algorithms and novel tangible interaction
modalities relate to subjective and physiological indices of
operator cognitive load (i.e., NASA Task Load Index, heart
rate variability). Our findings suggest that there are differences
in workload across conditions; however, subjective and cardiac
measures appear to be sensitive to different aspects of cognitive
state. The results hint at the potential of both tangible interfaces
and automation to engage operators and reduce cognitive load,
yet show the need for further validation of workload measures
for use in studying and optimizing human-swarm interactions.

Examining Audio Communication Mechanisms for Supervising Fleets of Agricultural Robots

During the COVID-19 pandemic, many research areas that require in person experiments with human volunteers have been impacted due to lockdowns and other activity-restricting policies. The field of robotics is no exception, and specially human-robot interaction research has been severely impacted. In order to circumvent the difficulty of gathering volunteers in person to interact with a robot, we have decided to build a novel crowdsourcing web platform for hosting our "Talk to Kotaro" experiment. The experiment consists of volunteers talking to a robot avatar and reacting to its semantic-free utterances. The developed web platform, which was built using the Python Flask framework, allows for such interactions while recording audio and video and other relevant data, which will be used for studying human impression estimation on gibberish speech. This paper describes not only the experiment and its preliminary results, but the developed platform itself; such tool is essential during pandemics and very useful for regular times, because it enables crowdsourcing data from all over the world.

How a Social Robot's Vocalization Affects Children's Speech, Learning, and Interaction

Social robots are used to perform mobile manipulation tasks, such as tidying up and carrying, based on instructions provided by humans.
A mobile manipulation planner, which is used to exploit the robot's functions, requires a better understanding of the feasible actions in real space based on the robot's subsystem configuration and the object placement in the environment.
This study aims to realize a mobile manipulation planner considering the world state, which consists of the robot state (subsystem configuration and their state) required to exploit the robot's functions.
In this paper, this study proposes a novel environmental representation called a world state-dependent action graph (WDAG).
The WDAG represents the spatial and temporal order of feasible actions based on the world state by adopting the knowledge representation with scene graphs and a recursive multilayered graph structure.
The study also proposes a mobile manipulation planning method using the WDAG.
The planner enables the derivation of many effective action sequences to accomplish the given tasks based on an exhaustive understanding of the spatial and temporal connections of actions.
The effectiveness of the proposed method is evaluated through practical machine experiments performed.
The experimental result demonstrates that the proposed method facilitates the effective utilization of the robot's functions.


A Modular Interface for Controlling Interactive Behaviors of a Humanoid Robot for Socio-Emotional Skills Training

As human–agent collaborations become more prevalent, it is increasingly important for an agent to be able to adapt to their collaborator and explain their own behavior. In order to do so, they need to be able to identify critical states during the interaction that call for proactive clarifications or behavioral adaptations. In this paper, we explore
whether the agent could infer such states from the human's eye gaze for which we compare gaze patterns across different situations in a collaborative task. Our findings show that the human's gaze patterns significantly differ between times at which the user is confused about the task, times at which the agent makes an error, and times of normal workflow. During errors the amount of gaze towards the agent increases, while during confusion the amount towards the environment increases. 
We conclude that these signals could tell the agent what and when to explain.

Analysing Eye Gaze Patterns during Confusion and Errors in Human–Agent Collaborations

In human-robot conversation, the human partner may ask any question within a specific task and expects an answer, ideally alongside a reason for that answer. For example, in a care home for the elderly, a human user might ask a companionship robot, "Where is my mug?", to which the robot would respond, "It is on the counter." For a given interaction to be smooth, a robot should respond correctly and be able to motivate its response, particularly in the cases of ambiguities. When providing answers to a user's question about a given scene, depth plays a critical role as spatial relations within the scene might change with depth as opposed to viewing the scene in 2D. For instance, as shown in Figure 1, when a user asks which object is closest to the white chair, 2D data might be insufficient, but incorporating 3D information can lead to a higher degree of correct responses provided by the robot in response to a human, and thus improve the quality of the interaction.

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