Italy

Theory of Mind is the process of ascribing mental states to other individuals we interact with. It is used for sense-making of the observed actions and prediction of future actions. Previous studies revealed that humans mentalize about artificial agents, but it is not entirely clear how and to what extent. At the same time mentalizing about humans is often influenced by biases such as an egocentric bias. 
We present a study investigating differences in participants&#39; ToM and their susceptibility to an egocentric bias when observing humans vs robots.
The participants observed an autonomous robot, a controlled robot, and a human in the same scenarios. The agents had to find an object in a laboratory. While watching the agents, participants had to make several action predictions as an implicit measure of ToM, potentially revealing an egocentric bias.
At the end, questions about the agent’s responsibility, awareness and strategy were asked. 
The results indicate that while participants generally performed ToM for all types of agents,
both the scenario as well as the agent type appear to influence participants&#39; likelihood of exhibiting an egocentric bias.

IEEE RO-MAN 2022

Differences and Biases in Mentalizing About Humans and Robots

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**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! 

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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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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.

Theory of Mind is the process of ascribing mental states to other individuals we interact with. It is used for sense-making of the observed actions and prediction of future actions. Previous studies revealed that humans mentalize about artificial agents, but it is not entirely clear how and to what extent. At the same time mentalizing about humans is often influenced by biases such as an egocentric bias. 
We present a study investigating differences in participants' ToM and their susceptibility to an egocentric bias when observing humans vs robots.
The participants observed an autonomous robot, a controlled robot, and a human in the same scenarios. The agents had to find an object in a laboratory. While watching the agents, participants had to make several action predictions as an implicit measure of ToM, potentially revealing an egocentric bias.
At the end, questions about the agent’s responsibility, awareness and strategy were asked. 
The results indicate that while participants generally performed ToM for all types of agents,
both the scenario as well as the agent type appear to influence participants' likelihood of exhibiting an egocentric bias.

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)

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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.

Sex dolls are passively jointed human-like structures. Their limited but non negligible dissemination may suggest future new modes of sexual and affective relationships with sex robots. Understanding the quality of the relationship between a owner and his/her sex doll could help us to anticipate the social consequences of future sex robots. In this framework, we propose, from available testimonies reporting a large range of behaviors including sexual relationships, destructive behaviors, or installation of the doll in the daily life of the owner, or romance with the doll generally expressed by photographies, to interpret the sex doll as a new form of transitional object in the words of Winnicott. Moreover, the hyper-sexualized appearance of the doll makes easier its fetishization as a “labor of signification” according to Baudrillard’s semiotic theory. If to cherish a transitional object in adulthood can be an indicator of depressive syndromes or borderline personality disorder, no emotional disorder is necessary linked to its private use. Further studies about sex dolls and robots could so take advantage of work that was already done with other forms of transitional objects for adults.


We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction

Enabling robots to identify when humans need
assistance is key to being able to provide help that is both
proactive and efficient. This challenge is particularly difficult
for humans eating a meal in a restaurant, a context which is
dense with interlaced social elements such as conversation in
addition to functional tasks such as eating. We investigated the
challenge of identifying human dining activities from singleviewpoint
footage by collecting and annotating the individual
activities of five two-person meals. From this process, we found
that addressing the question of identifying meal phases and
overall neediness requires identifying an underlying group state
for the table as a whole. We report on the individual activities
and group states, as well as the interdependencies between these
factors that can be leveraged to both provide and measure
effective robotic restaurant service. In addition to the insights
revealed by this dataset, we describe preliminary attempts to
create an automated classification system for these activities.

How Users, Facility Managers, and Bystanders Perceive and Accept a Navigation Robot for Visually Impaired People in Public Buildings

his study explores how to best utilize service robots when deploying them in customer facing contexts for information requests. Many companies investing in such robots unfortunately fail to integrate them successfully in a way where they facilitate meaningful interactions with users. Copenhagen Visitor Service is one of those companies who have tried to integrate a Pepper robot into their service environment but failed due to several factors relating to both the environment, the robot and lack of a strategical implementation. Through an exploratory field study with in-situ feedback from the participants, it became evident that the robot in this specific setting needed a more focused purpose and an interaction flow that was not reliant on verbal communication. A prototype was developed consisting of an informative quiz targeting families. A subsequent field deployment demonstrated a better and more meaningful user experience, indicating that careful consideration of the use context is paramount for a successful integration as well as a strategical implementation.

Ankle Intention Detection Algorithm with HD-EMG Sensor

Simulation tools are essential for robotics research, especially for those domains in which safety is crucial, such as Human-Robot Collaboration (HRC). However, it is challenging to simulate human behaviors, and existing robotics simulators do not integrate functional human models. This work presents Open-VICO, an open-source toolkit to integrate virtual human models in Gazebo focusing on vision-based human tracking. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multi-camera vision setups, and human-tracking techniques along with numerous robot and sensor models thanks to Gazebo. The possibility to incorporate pre-recorded human skeleton motion with Motion Capture systems broadens the landscape of human performance behavioral analysis within Human-Robot Interaction (HRI) settings. To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction example. The key of this work is to create a straightforward pipeline which we hope will motivate research on new vision-based algorithms and methodologies for lightweight human-tracking and flexible human-robot applications.

Open-VICO: An Open-Source Gazebo Toolkit for Multi-Camera-based Skeleton Tracking in Human-Robot Collaboration

The ability to impute mental states to oneself or others, or Theory of Mind (ToM), has been intrinsically linked
to trust between humans. However, less is known about how a robot mimicking ToM affects users’ trust and behaviour.
We explore this through an online study, where we compare three robot personas in a cooperative maze navigation task: one neutral, one that explains its reasoning in technical terms, and one that mimics ToM. We show that ToM influences human decision-making behaviour and trust in a way that makes it more appropriate with respect to the competencies of the robot. This is key for human-robot collaboration and adoption of robotics moving forward.

Exploring Theory of Mind for Human-Robot Collaboration

In this study, we extend our robot trust model into
a multimodal setting in which the Nao robot leverages audiovisual data to perform a sequential multimodal pattern recalling
task while interacting with a human partner who has different
guiding strategies: reliable, unreliable, and random. Here, the
humanoid robot is equipped with a multimodal auto-associative
memory module to process audio-visual patterns to extract
cognitive load (i.e., computational cost) and an internal reward
module to perform cost-guided reinforcement learning. After
interactive experiments, the robot associates a low cognitive load
(i.e., high cumulative reward) yielded during the interaction
with high trustworthiness of the guiding strategy of the partner.
At the end of the experiment, we provide a free choice to the
robot to select a trustworthy instructor. We show that the robot
forms trust in a reliable partner. In the second setting of the
same experiment, we endow the robot with an additional simple
theory of mind module to assess the efficacy of the instructor
in helping the robot perform the task. Our results show that
the performance of the robot is improved when the robot bases
its action decisions on factoring in the instructor assessment.

Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

Expressiveness is a critical feature for the communication between humans and robots, and it helps humans to better understand and accept a robot. Emotions can be expressed through a variety of modalities: kinesthetic (via facial expression), body posture and gestures, auditory, thus the acoustic features of speech, and semantic, thus the content of what is said. One of the most effective modalities to communicate emotions is through facial expressions. Social robots often show facial expressions with coded animations. However, the robot must be able to express appropriate emotional responses according to the interaction with people. In this work, we consider verbal interactions between humans and robots and propose a system composed of two modules for the generation of facial emotions by recognising the arousal and valence values of a written sentence. The first module, based on Bidirectional Encoder Representations from Transformers, is deployed for emotion recognition in a sentence. The second, an Auxiliary Classifier Generative Adversarial Network, is proposed for the generation of facial movements for expressing the recognised emotion in terms of valence and arousal.

Action Unit Generation through Dimensional Emotion Recognition from Text

This paper examines how conversational agents (CAs) can communicate emotions non-verbally using light as communication modality. Therefore, we manipulated the CA Alexa to demonstrate emotions (joy and sorrow) using different light modalities (Echo Dot ring, a Hue lamp and the combination) with either a congruent verbal context (party or funeral) or no verbal context. In an online study 167 participants evaluated the perceived emotion of Alexa, their own emotional state as well as the perception of Alexa after watching a video with a user interacting with Alexa. Although the perceived emotions of Alexa were not affected by the experimental conditions, the results indicate that the perception of Alexa as well as the user’s emotion is affected by the displayed communication modality. As external light can be used to manipulate the users’ perception of CAs, the findings give relevant implications for the design of CAs.

Evaluating the Effect of Theory of Mind on Peoples Trust in a Faulty Robot

From RViz to augmented reality (AR), a wide variety of robot \textcolor{black}{signal visualizations exist for conveying robot capabilities. Many of the visualizations designed for AR, however, have not isolated multiple salient Virtual Design Elements (VDEs) for a given signal and comparatively evaluated combinations of those VDEs. To address this, we identify multiple VDEs for AR signaling of the following core robot capabilities:} \textit{navigation}, \textit{light detection and ranging (LiDAR)}, \textit{camera}, \textit{face detection}, \textit{audio localization}, and \textit{natural language processing}. We evaluated each signal's VDE combinations with an Amazon Mechanical Turk study ($n$=150) where participants watched 4 videos for each signal (consisting of 2 independent VDE choices) and rated the clarity and visual appeal of each signal. The results define a set of the most clear and visually appealing signal visualization designs and inform about interaction effects among VDEs. The resulting VDEs offer design insights and a baseline for continued research into AR robot capability signalling.

Human-Robot Handovers using Task-Space Quadratic Programming

Mobile robots must navigate efficiently, reliably, and appropriately around people when acting in shared social environments. For robots to be accepted in such environments, we explore robot navigation for the social contexts of each setting. Navigating through dynamic environments solely considering a collision-free path has long been solved. In human-robot environments, the challenge is no longer about efficiently navigating from one point to another. Autonomously detecting the context and adapting to an appropriate social navigation strategy is vital for social robots' long-term applicability in dense human environments. As complex social environments, museums are suitable for studying such behavior as they have many different navigation contexts in a small space.

Our prior Socially-Aware Navigation model considered context classification, object detection, and pre-defined rules to define navigation behavior in more specific contexts, such as a hallway or queue. This work uses environmental context, object information, and more realistic interaction rules for complex social spaces. In the first part of the project, we convert real-world interactions into algorithmic rules for use in a robot's navigation system. Moreover, we use context recognition, object detection, and scene data for context-appropriate rule selection. We introduce our methodology of studying social behaviors in complex contexts, different analyses of our text corpus for museums, and the presentation of extracted social norms. Finally, we demonstrate applying some of the rules in scenarios in the simulation environment.

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We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction

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Less Egocentric Biases in Theory of Mind When Observing Agents in Unbalanced Decision Problems

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