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Affective Computing and Human Robot Interaction a short introduction Joost Broekens Telematica Institute, Enschede, LIACS, Leiden University. Overview • What is emotion? • What theoretical angles can we take to study emotion? • What is affective computing + examples ? • Why is affective computing useful? 15 min coffee break. • Interactive Human-Robot Interaction • Human emotional expressions as feedback to a learning robot Emotion (what is it)? • Common emotions: fear, anger, happiness, sadness, surprise, disgust • Short episode of synchronized system activity triggered by event: – – – – – • subjective feelings (the emotion we normally refer to), tendency to do something (action preparation), facial expressions, evaluation of the situation (cognitive evaluation, thinking), physiological arousal (heartbeat, alertness). Affect (affectie)= related to emotion and mood: – emotion – mood – affect terms of, : short term, high intensity, object directed, : unfocused, long term, low intensity, : sometimes seen as abstraction of emotion/mood in • positiveness/negativeness and activation/deactivation (Russell). Emotion (why do we have it)? • Situational evaluation and communication. • Heuristic relating events to personal goals, needs and beliefs: – evaluates personal relevance of event (Scherer) and helps decisionmaking (Damasio), – fast reactions and action preparation (Frijda), – influence information processing and decision making (Damasio): • e.g, adaptation, attention, search. • Communication medium: – – – – communicate internal state (show feelings), alert others (direct attention), show empathy (show understanding of situation). Give feedback (evaluate behavior). Emotion (what does it do)? • Emotion and affect influence thought and behavior: – The kind of thoughts we have • Mood congruency (e.g., positive moods triggers positive thought) – The way we process information • Broad vs. narrow look (Goschke & Dreisbach) (forest vs. trees) • A lot vs. a little processing effort (Scherer, Forgas) – What we think about things • Emotion/mood as information (Clore & Gasper) (how does this feel?) • Emotion as belief anchor (Frijda & Mesquita) (idea “fixation”) – How we learn and adapt • Emotion/affect as social reinforcement (emotion expression as signal) • Emotion/affect as intrinsic reinforcement (use my own emotion as feedback) • Emotion as “metaparameter” to control learning process (explore vs. exploit) Emotion (how can we study it)? • Biological/Neurological (Damasio, Panksepp, LeDoux, Rolls): – aim: find the necessary / sufficient brain areas and circuits involved in emotions /feelings / self-regulation / adaptation. • Biological/Evolutionary (Darwin, Ekman): – aim: why emotions exist in the first place, what’s the utility of emotion. • Cognitive/Psychological (Scherer, Frijda): – aim: understand relation cognition/emotion, why does event e results in emotion x while: • the same event e may result in a different emotion, and • other events may result in the same emotion x. • Social (Ekman, and others): – aim: understand the role of emotion in communication. What is affective computing? • Computing that relates to, arises from, or deliberately influences emotions (Picard). • Different types of computer models: – – – – – recognize emotions (perception, in), interpret/elicit emotions (cognition/behavior, processing), emotional influence on behavior (adaptation, attention), show emotions (expression, out), and complex mixtures of these… • An affective computing system is – a system of computational processes that perceives, expresses, interprets, or uses emotions, – e.g. a robot, a virtual character, a tutor agent, a fridge, etc… Some examples… SIMS 2 (Electronic Arts) • Entertainment: emotions are used to provide entertainment value. Kismet (Breazeal) • Social: Kismet, A framework, using a humanoid head expressing emotions, to study: – effect of emotions on human-machine interaction. – learning of social robot behaviors during human-robot play. – joint attention. Mission Rehearsal Exercise (Gratch & Marsella) • Cognitive: study the influence of artificial emotions on – planning mechanism of virtual characters, – training effect on trainees (emotion might enhance effect) Assistive robotics • Aibo (Sony, Japan) Entertainment robot • I-Cat (Philips, NL) Robot assistant for elderly people • Paro (Wada et al, Japan) Robot companion for elderly • Huggable (MIT, USA) Robot companion for elderly Why is affective comp. useful (1)? • From a theoretical point of view… • Advance emotion theory – simulated agents that elicit emotions to study possible psychological structure of emotions (Scherer), – emotion as artificial motivator (e.g. a bored robot that explores) (Canamero). • Understand intelligence and adaptation – laws and rules are not sufficient for understanding or predicting human behavior and intelligence (e.g. how to sift thru many possible choices) (Damasio, Picard) – simulated learning agents influenced by emotions (e.g., emotion as reward) (Breazeal, Broekens), – Artificial Intelligence, Artificial Life? Why is affective comp. useful (2)? • From a practical point of view… • Facilitate Human-Computer (Robot) interaction – use emotions in simulated-agent plans (to simulate human reasoning) (Gratch & Marsella), – communication and joint attention (Breazeal, MIT) – robot acceptation (Heerink, Telin) – Persuasive design (VR training, tutor agents (Gratch & Marsella, Nijholt)) – Interactive robot learning (second part of this lecture). • Entertainment – Computer games such as the Sims (EA), Aibo Robot (Sony). Affective Computing in short… • Affect has different meanings related to emotion. – emotion – mood – affect : short term, high intensity, object directed, : unfocused, long term, low intensity, : sometimes seen as abstraction of emotion/mood in terms of, • positiveness/negativeness and activation/deactivation. • Affective computing is about computing with emotions – “a system of computational processes that perceives, expresses, interprets, or uses emotions” • Why? – – – – advance emotion theory, understand intelligence, facilitate Human-Computer (Robot) interaction, entertainment. Interactive Robot Learning (Real-time interaction to control robot behavior) Interactive Robot Learning: a special case of HRI • Goals Human Robot Interaction: – Develop more natural and effective ways of interacting between robots and humans. – Allow non-expert interaction. – Allow flexibility of robot behavior. • How? – – – – Gesture recognition Cognitive robot architectures Machine learning (artificial adaptation) Affective computing • Emotion recognition • Emotion expression Example, Guidance and Feedback • Interactive robot learning – Learning by Example • Imitation learning (see Breazeal & Scassellati) • E.g., gesture repetition – Learning by Guidance (Thomaz & Breazeal) • • • • In general: future directed learning cues, such as Directing attention Communicating motivational intentions (e.g., anticipatory reward) Proposing actions – Learning by Feedback • Learn by trial (+feedback) and error (-feedback) and exploration. • Additional human-based reinforcement signal (Breazeal & Velasquez; Isbell et al; Mitsunaga et al; Papudesi & Hubert) Why study Interactive Robot Learning? • Robot perspective: Interactive robot learning – Facilitate human-robot interaction – Study learning and adaptation – Future: • enable robots to interactively cooperate in an efficient manner with humans in ways that are natural to humans. • Psychological perspective: Understanding mechanisms – – – – – How can affect influence learning? How can affect influence attention processes? Joint Human-Robot attention Study learner-teacher relations Etc.. EARL • A framework to study the influence of Emotion on Adaptive behavior in the context of Reinforcement Learning: – – – – emotion as reinforcement (reward/punishment), emotion as influence on learning process, emotion as information, artificial emotion resulting from learning process EARL • A typical EARL setup: – Simulated robot in a – Maze learning (navigation) tasks – Reinforcement learning (RL) approach to robot learning • Learn by trial (+feedback) and error (-feedback) and exploration. – Robot has own model of emotion – Robot head to express emotion – And… • Webcam and emotion recognition to interpret emotions Experiment: Human Affect as Reinforcement to Robot • An experiment to study interactive robot learning • Simulated Robot learns to find food in a maze. – Explore unknown environment (try actions). – Reinforcement Learning • Feedback from environment based on taken actions • + feedback=repeat • - feedback=don’t repeat – Learn which sequence of actions gives best +feedback. • Robot also uses human facial expressions as +/- feedback Facial Expression as Reinforcement • Affective signal as additional reinforcement Sad / happy – Web cam – Emotional expression analysis -/+ – Positive emotion (happy) = reward – Negative emotion (sad) = punishment Robot behavior to screen – So: in addition to feedback from environment emotional expression is used in learning as rhuman, a social reward coming from the human observer Reinforcement-based robot learning Food (+) 2 Agent Wall (-) path Human Start Feedback Path b human feedback wall food Experiment: results • Test learning difference between standard agent and social agents – Standard agent uses rewards environment to update behavior. – Social agent uses rewards from environment and reward from human emotional signal. • Results: – Social agent learns the path to the food quicker (just believe me, so that I don’t have to explain the graphs) • Earl demo… Interactive Robot Learning in short… • A special case of Human Robot Interaction – Goal HRI: more efficient, flexible, personal, pleasant human robot interaction – Interactive Robot Learning: Imitation, Feedback, Guidance. • Affective Interaction Feasible and useful – can be used as guidance (MIT robot studies), and – feedback (experiment shown). • Why? – Robot perspective • Facilitate human-robot interaction • Study learning and adaptation – Psychological perspective • • • • How can affect influence learning? How can affect influence attention processes? Joint Human-Robot attention Study learner-teacher relations