Download Affective computing and HRI (HCI2007) 2x45min lecture

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:
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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:
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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:
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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…
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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?
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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?
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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)
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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
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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:
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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…
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A special case of Human Robot Interaction
– Goal HRI: more efficient, flexible, personal, pleasant human robot interaction
– Interactive Robot Learning: Imitation, Feedback, Guidance.
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Affective Interaction Feasible and useful
– can be used as guidance (MIT robot studies), and
– feedback (experiment shown).
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Why?
– Robot perspective
• Facilitate human-robot interaction
• Study learning and adaptation
– Psychological perspective
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How can affect influence learning?
How can affect influence attention processes?
Joint Human-Robot attention
Study learner-teacher relations