Affective Computing and Human Robot Interaction a short introduction a short introduction Joost...
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Transcript of Affective Computing and Human Robot Interaction a short introduction a short introduction Joost...
Affective Computing andHuman Robot Interaction
a short introductionJoost 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 : short term, high intensity, object directed,– mood : unfocused, long term, low intensity,– affect : sometimes seen as abstraction of emotion/mood in terms of,
• 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 decision-
making (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…
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 : short term, high intensity, object directed,– mood : unfocused, long term, low intensity,– affect : 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: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– Web cam
– Emotional expression analysis
– Positive emotion (happy) = reward
– Negative emotion (sad) = punishment
– So: in addition to feedback from environment emotional expression is used in learning as rhuman, a social reward coming from the human observer
Sad / happy
- / +
Robot behaviorto screen
Reinforcement-based robot learning
path wall food humanfeedback
2
b
Food (+)
Agent
Wall (-)
HumanFeedback
Path
Start
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