Sociable Machines Cynthia Breazeal MIT Media Lab Robotic
Sociable Machines Cynthia Breazeal MIT Media Lab Robotic Presence Group Human-Robot Relations Hondas Asimo Sonys Aibo iRobot Robotic extensions Types of relationships
Face to face with a robot creature Embodied, distal interactions through a robot avatar Augmented physicality through robotic extensions Capable machines, untrained users, human environment Balance human strengths with machine capabilities Useful and enjoyable! Cynthia Breazeal, MIT Media Lab Robots in your everyday life Competence in: Untrained users of different
Appropriate mental model Supports what comes naturally On the job learning Age, gender, culture, etc. Human centered design
Human engineered environment Human social environment Easy to teach Long-term relationships Acceptance, trust Cynthia Breazeal, MIT Media Lab Sociable anthropomorphic robots Very complex technology Social interface is (Reeves&Nass)
Humanoid robots are well suited to this hypothesis Intuitive, natural Untrained users Same morphology, sensing Share social, communication cues HRI meets HCI
Study how people want to and do interact with them. Informs design Evaluation methods Cynthia Breazeal, MIT Media Lab Three research themes Informed by scientific understanding of humans And animals SCIENCE HCI
Evaluate robot compatibility with people ROBOTICS & AI Build robots that do real things In the real world with real people Cynthia Breazeal, MIT Media Lab Issues for sociable robots today The real-world is Robots have limited abilities compared to people
Complex Ever-changing Motor skills Perceptual abilities Mental abilities Imbalance in social sophistication Yet, social interaction is Tightly coupled
Mutually regulated Cynthia Breazeal, MIT Media Lab Early exploration into sociable humanoids Set appropriate expectations Use of expressive feedback to regulate interaction
Kismet, MIT AI Lab Not human Robo-baby Emotive expressions Communicative displays Paralinguistic cues Use science of natural behavior as a guide Start simple and learn, develop Cynthia Breazeal, MIT Media Lab Socially situated learning: A path to more capable machines?
Issues for learning systems (robots or otherwise) Knowing what matters Knowing what action to try Evaluating actions Correcting errors Recognize success Structuring learning If task is pre-specified, then can do at design-time
If not the case, then what? Address issues through structured social interactions Robots in a benevolent learning environment Cynthia Breazeal, MIT Media Lab Learning from the way we teach Cynthia Breazeal, MIT Media Lab Social skills that support learning Direct visual attention
Indicates saliency (i.e.what matters) Match to human find similar things interesting Robot responds to attention directing cues of people Robot sends feedback to person for focus of attention Cynthia Breazeal, MIT Media Lab Video of attention system Cynthia Breazeal, MIT Media Lab Social skills that support learning
Recognize communicated reinforcement Serves as progress estimator Serves as signal for goal attainment Robot should recognize affective feedback from human Robot signal to human that intent was properly understood Cynthia Breazeal, MIT Media Lab Video of communicated affect
Cynthia Breazeal, MIT Media Lab Social skills that support learning Communicate internal state to human Allows human to: Predict and understand robots behavior Tune own behavior to robot Improves quality of interaction Robot conveys internal state to human in an intuitive manner
Can be used by both to establish better quality instruction Cynthia Breazeal, MIT Media Lab Communication of internal state Cynthia Breazeal, MIT Media Lab Social skills that support learning Regulating the interaction Provides structure to the interaction Interactive games Variations on a theme
Avoid being overwhelmed or under-stimulated Turn-taking as cornerstone Human interaction Human instruction Cynthia Breazeal, MIT Media Lab Video of proto-conversations Cynthia Breazeal, MIT Media Lab Lessons from Kismet Face to face
In human terms Being and Feeling in communication Human drive to animate, anthropomorphize Importance of gaze Social qualities Emotive qualities
Physical interaction Expressive feedback is vital Entrainment and accommodation Mutual regulation Being engaged vs. interacting Cynthia Breazeal, MIT Media Lab Related, ongoing directions HRI gaze studies Smart Puzzle Fruit SCIENCE Organic Robots HRI & DESIGN ENGINEERING
Sensate Silicone Skin Sociable robots Cynthia Breazeal, MIT Media Lab Sociable Robots Stan Winston Studios Media Lab collaboration Next generation sociable robot Fully embodied Organic look and feel Highly expressive Socially situated learning
Cynthia Breazeal, MIT Media Lab Robot Avatars/Performers Stan Winston Studios Media Lab collaboration Symbiotic control Puppeteer and single-mind performance Human provides content, new interfaces Robot local intelligence to perform content
Physical medium for embodied interactions Visual, auditory, tactile Mobile Shared environment, reference frame Physical interactions with world and others Cynthia Breazeal, MIT Media Lab Organic Robots What gives a machine a living presence?
Organic qualities to make them familiar yet distinct Intriguing blend between plant and animal Silicone skin instead of plastic shells Natural and expressive movement, serpentine Visual perception of people (faces, movement, color) Cynthia Breazeal, MIT Media Lab Sensate Synthetic Skin Perhaps next to the brain, the skin is the most important of all our organ systems. Ashley Montagu, Touching: The Human Significance of the Skin, 1986, p.4 Sensate skin for environmental interactions Active perception of material characteristics (hard, soft) Development of novel conductive silicone sensor Neuro-physiological representations
Cynthia Breazeal, MIT Media Lab Human-Robot Interaction Studies Controlled studies to better understand the human side of human-robot interaction A series of studies to understand the human Focus on the important of gaze in interaction Compare physical (robot) verses virtual (animation) Examine arousal and engagement through autonomic responses To better understand the advantages and limitations of physical vs. animated media Cynthia Breazeal, MIT Media Lab
Raelene. Thompson. Australian Government Attorney-General's Department. Emergency Management Australia. Executive Director - Australian Emergency Management Institute (AEMI) The Australian Emergency . Management Institute, Mount Macedon, Victoria, Australia.
Meta-analysis of studies of children with adequate decoding ability, but poor reading comprehension (Spencer & Wagner, 2018) Based on 86 studies, the children with comprehension problems tended to have deficits in oral language
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