Moirai: Negotiation-through-Interaction for Healthy Exercizing

We present Moirai, a device that aids runners in self-regulating their running routine. By negotiating with the device, runners can balance their desire to achieve their goals while preventing overtraining injuries. The interaction relies on the strategic repertoire principle, a five-way taxonomy of strategies used in human- human negotiation. The user and the system can use the following strategies: contending, conceding, compromising, problem-solving, and avoiding. Behavior change design predominantly focuses on the first two: contending relates to hard paternalism, where the user has no choice but to accept, and conceding to soft paternalism or nudging, where the user can deviate from a default. The other three strategies are relatively underexplored, the closest to some of these being the aesthetic of friction. Relying on data and human negotiation strategies to reach a goal that is not solely one of the users, Moirai can appear as a moral agent. We envision these principles as applicable beyond the exercising context to other domains that require self-regulation, such as health, sustainability, or productivity- related applications

Improving Collaboration Experiences and Skills: An Open-ended, User-Driven Self-Tracking Approach for Education

editorial reviewedCollaboration is considered an essential skill necessary for work and life in the 21st century. It is hence necessary to support students in developing this skill, whose necessity is amplified by hybrid work and globalization. In this vision, we bring insights and practices from the personal informatics field to the education domain, in order to trigger self-Awareness and collective sensemaking. We propose CoSensUs, a physical self-Tracking kit for teams of students to track and reflect on their collaboration practices and experiences. We argue for a user-driven, open-ended, playful, and privacy-centered solution, which would track and visualize data on a group level. This original and underexplored focus on group-level tracking also aims to account for the special needs of students, subject to social pressure and potential control in institutional settings. Through this vision, we contribute to the development of essential social and collaboration skills, in an original, playful and inclusive way

It's Food Fight! Introducing the Chef's Hat Card Game for Affective-Aware HRI

Emotional expressions and their changes during an interaction affect heavily how we perceive and behave towards other persons. To design an HRI scenario that makes possible to observe, understand, and model affective interactions and generate the appropriate responses or initiations of a robot is a very challenging task. In this paper, we report our efforts in designing such a scenario, and to propose a modeling strategy of affective interaction by artificial intelligence deployed in autonomous robots. Overall, we present a novel HRI game scenario that was designed to comply with the specific requirements that will allow us to develop the next wave of affective-aware social robots that provide adequate emotional responses.Comment: Accepted by the Workshop on Exploring Creative Content in Social Robotics at HRI202

Negotiating Learning Goals with Your Future Learning-Self

This paper discusses the challenges towards designing an educational avatar which visualizes the future learning-self of a student in order to promote their self-regulated learning skills. More specifically, the avatar follows a negotiation-based interaction with the student during the goal-setting process of self-regulated learning. The goal of the avatar is to help the student get insights of their possible future learning-self based on their daily goals. Our approach utilizes a Recurrent Neural Network as the underlying prediction model for expected learning outcomes and goal feasibility. In this paper, we present our ongoing work and design process towards an explainable and personalized educational avatar, focusing both on the avatar design and the human-algorithm interactions

Improving emotional expression recognition of robots using regions of interest from human data

This paper is the first step of an attempt to equip social robots with emotion recognition capabilities comparable to those of humans. Most of the recent deep learning solutions for facial expression recognition under-perform when deployed in Human-Robot-Interaction scenarios, although they are capable of breaking records on the most varied benchmarks on facial expression recognition. The main reason for that we believe is that they are using techniques that are developed for recognition of static pictures, while in real-life scenarios, we infer emotions from intervals of expression. Utilising on the feature of CNN to form regions of interests that are similar to human gaze patterns, we use recordings from human-gaze patterns to train such a network to infer facial emotions from 3 seconds video footage of humans expressing 6 basic emotions

You Were Always on My Mind: Introducing Chef’s Hat and COPPER for Personalized Reinforcement Learning

Reinforcement learning simulation environments pose an important experimental test bed and facilitate data collection for developing AI-based robot applications. Most of them, however, focus on single-agent tasks, which limits their application to the development of social agents. This study proposes the Chef’s Hat simulation environment, which implements a multi-agent competitive card game that is a complete reproduction of the homonymous board game, designed to provoke competitive strategies in humans and emotional responses. The game was shown to be ideal for developing personalized reinforcement learning, in an online learning closed-loop scenario, as its state representation is extremely dynamic and directly related to each of the opponent’s actions. To adapt current reinforcement learning agents to this scenario, we also developed the COmPetitive Prioritized Experience Replay (COPPER) algorithm. With the help of COPPER and the Chef’s Hat simulation environment, we evaluated the following: (1) 12 experimental learning agents, trained via four different regimens (self-play, play against a naive baseline, PER, or COPPER) with three algorithms based on different state-of-the-art learning paradigms (PPO, DQN, and ACER), and two “dummy” baseline agents that take random actions, (2) the performance difference between COPPER and PER agents trained using the PPO algorithm and playing against different agents (PPO, DQN, and ACER) or all DQN agents, and (3) human performance when playing against two different collections of agents. Our experiments demonstrate that COPPER helps agents learn to adapt to different types of opponents, improving the performance when compared to off-line learning models. An additional contribution of the study is the formalization of the Chef’s Hat competitive game and the implementation of the Chef’s Hat Player Club, a collection of trained and assessed agents as an enabler for embedding human competitive strategies in social continual and competitive reinforcement learning

Negotiating Learning Goals with Your Future Learning-Self

This paper discusses the challenges towards designing an educational avatar which visualizes the future learning-self of a student in order to promote their self-regulated learning skills. More specifically, the avatar follows a negotiation-based interaction with the student during the goal-setting process of self-regulated learning. The goal of the avatar is to help the student get insights of their possible future learning-self based on their daily goals. Our approach utilizes a Recurrent Neural Network as the underlying prediction model for expected learning outcomes and goal feasibility. In this paper, we present our ongoing work and design process towards an explainable and personalized educational avatar, focusing both on the avatar design and the human-algorithm interactions

It's food fight! Designing the chef's hat card game for affective-aware HRI

This paper describes the design of an interactive game between humans and a robot that makes it possible to observe, analyze, and model competitive strategies and affective interactions with the aim to dynamically generate appropriate responses or initiations of a robot. We apply an iterative design process that applied several pilot evaluations to define the requirements for the game with a theme, mechanics, and rules that motivate a choice between competition and cooperation and provokes emotional reactions even after subsequent games. Also, the game is designed to be easily understood by humans and unambiguously interpreted by machines. Overall, we aim to make the Chef's Hat card game a standard platform for the development of cooperative/competitive and emotionally aware agents and enable embodied interaction between multiple humans and robots