The Influence of Emotions in Humanoid Behavior and for the Development of a Robotic Self

In this workshop, some fundamental principles of human emotional intelligence will be initially presented. We will discuss the influence of emotions in human decision-making and in the formation of consciousness following several neuroscientific findings and theory of mind. Then, we will see how to apply these theories on a cognitive architecture for social robots. We will focus on the somatic marker hypothesis of Antonio Damasio and an example of its implementation. Then, we will discuss the SEAI (Social Emotional Artificial Intelligence) cognitive architecture to see how it is possible to implement some characteristic human behavior in an expressive humanoid. This system is conceived as a hybrid deliberative/reactive architecture that based on information gathered by the environment can perform high-level reasoning on the perceived social and emotional context, and make the robot react accordingly. Some experiments conducted with the social robots FACE (Facial Automaton for Conveying Emotions) and Abel will be presented, to finally discuss how we can endow a robot with a specific personality and possibly generate some form of robotic self. The aim is that of modelling human cognition to improve the perception of interacting with an autonomous believable agent, therefore, an open discussion on what this could imply in HRI will be certainly triggered.

Bio:

MD in Biomedical Engineering (2014) and PhD in Information Engineering (2018) both at the University of Pisa, Lorenzo Cominelli is a post-doc researcher at the “E. Piaggio” Research Center. His research is focused on AI and cognitive systems for social robots, with a particular attention to the influence of emotions in the robot decision-making. He has been responsible for the design and development of SEAI (Social Emotional Artificial Intelligence) – the FACE robot's cognitive architecture. Since 2020 he is working on the development of Abel, a hyper-realistic expressive robot born by the collaboration between the research center and the animatronics creator Gustav Hoegen. Abel will use SEAI and its upgraded versions for applications in medical settings (e.g., therapy for children with neurodevelopmental disorders, elderly people with neurodegenerative diseases) and for studies on cognitive robotics and human-robot interaction.

Who is Afraid of Non-Universal (Deep Learned) Facial Perception?

Facial Expression Recognition (FER) has become a popular topic within the hyper-active computer vision community, which has led to the development of a plethora of FER solutions easily accessible to the general public. In most cases, based on deep learned facial expression representations. Such solutions became the backbone of human-based interaction research, being used as means for human behavior analysis, the backbone for interaction-driven models, and one of the most fundamental blocks of proposed cognitive architectures. Most of these important research rely blindly on the objective performance of FER systems, and their capability to categorize a face, in most cases even on a frame-level, into one known and pre-determined emotional category. Once you actually understand how deep-learned FER models actually categorize faces, it is easy to see that trusting on their outputs might bias drastically all of the previously mentioned research areas. These models are trained mostly in a supervised task, where groups of pixels are pushed to compose a specific and pre-determined emotional category. In most cases, these affective labels are deeply connected to the scenario represented by the datasets these models were trained on, which changes drastically the interpretation of their FER results. Similarly to the recent advents on non-universal facial perception, understanding the context in which these models were trained might help to avoid a strong bias in their application on fundamental research, and help us to be more responsible in our claims and findings. The goal of this talk is to discuss the core of the problem of trusting blindly FER systems, and to foster a discussion on the importance of understanding their functioning. In this regard, I will present our most recent research on facial expression perception and hot we can address the biasing of affective categorization based on the non-universal perception theory, and how this can impact in future use of FER technology to other fields.

Bio:

Pablo Barros is a machine learning scientist working at Sony R & D Center in Brussels, focusing on physiological signal processing for mental health solutions. Pablo holds a Ph.D. degree in computer science from the University of Hamburg, in Germany, and over the last years, has worked on different projects involving social robotics and affective computing. In particular, his research focuses on social perception, in particular facial expression recognition, but also on modelling, based on artificial intelligence, the role of social agents while interacting with humans.

Dr. Ana Tanevska is a postdoctoral researcher at the Italian Institute of Technology in Genoa (Italy), within the ERC-funded project wHiSPER, investigating shared perception between humans and robots. Ana obtained their PhD degree in Bioengineering and Robotics from the University of Genoa in March 2020, with the thesis "Towards a Cognitive Architecture for Socially Adaptive Human-Robot Interaction".
Prior to moving to Italy, Ana obtained a B.Sc. degree in Computer Science and Engineering (2015) and a M.Sc. degree in Intelligent Systems Engineering and Robotics (2016), both from FCSE (FINKI) in Skopje, Macedonia.
Ana's main research interests include cognitive robotics, adaptation in human-robot interaction (HRI), and socially-assistive robotics. Their work on socially-adaptive cognitive architectures has been most recently published in Frontiers in Robotics and AI.

Prof. Lola Cañamero is Chair of Robotics and Neuroscience at CY Cergy Paris University, where she is a member of the Neurocybernetics Group in the ETIS Laboratory. She was previously Reader in Adaptive Systems and Head of the Embodied Emotion, Cognition and (Inter-)Action Lab in the Department of Computer Science at the University of Hertfordshire in the UK, which she joined as faculty in 2001.
She holds an undergraduate degree (Licenciatura) in Philosophy from the Complutense University of Madrid and a PhD in Computer Science (Artificial Intelligence) from the University of Paris-XI, France. She turned to Embodied AI and robotics as a postdoctoral fellow in the groups of Rodney Brooks at MIT (USA) and of Luc Steels at the VUB (Belgium). Since 1995, her research has investigated the interactions between motivation, emotion and embodied cognition and action from the perspectives of adaptation, development and evolution, using autonomous and social robots and artificial life simulations. She has played a pioneering role in nurturing the emotion modeling community. She is author or co-author of over 150 peer-reviewed publications in the above topics.