biologically inspired design for (more) scalable robots cba fall 2002 cynthia breazeal mit media lab...

Biologically Inspired Design for (more) Scalable Robots

CBA Fall 2002

Cynthia BreazealMIT Media Lab

Robotic Life Group

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Robotic Life GroupCBA Fall 2002

Robots Inspired by Nature

Robots as interesting complex systems Similarity to animals

Consequences of having a real body Real tasks in the real world --- cannot predict all interactions

Lessons learned from biological creatures Increase physical complexity Increase behavioral complexity

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Inspiration from Insects

Exploit physical modularity Complex robot made of simpler robots

Sensors Actuation Computation

Examples Hannibal Reconfigurable robots (Daniela Rus) Design by Evolution

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Adaptive, Distributed Control

No “homunculus”

Decompose complex robot control problem to coordination of several simpler control problems

Multi-joint coordination arises from interaction Through physical interactions from world and body Communication between simpler robot systems

Tolerant to external perturbations from tight coupling to real world

reactive

reflective

deliberative

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Cruse’s Model for Insect Locomotion

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Robust, Flexible Control

Smooth transitions for a family of wave gait Energy consumption Speed Stability

Extended to rough terrain

Robust & adaptive arm coordination Coupled neural oscillators Exploit physical coupling

Extends to multiple tasks Matt Williamson, MIT AI

Lab

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Tolerant to physical failure

Fault tolerant to sensor and actuator failure

Add internal assessment at perceptual level Low-level sense of “all is well” Self monitoring within virtual sensors

Exploit complementary sensory suites

Identify and use all working sensors in perceptual result

Address sensor failure at this level before effect of failure propagates

Leverage from distributed control to readapt behavior Adapt gait if catastrophic failure

Ground contact

Joint

angle

Angle

compressVertical

force

Power stroke

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Inspiration from Ethology

Lessons from insects Modularity, self regulation, and

internal assessment at reactive level Single goal: rough terrain locomotion

Lessons from Ethology Inspiration from behavior of birds, fish,

mammals Deliberative behavior

Survival in complex, sometimes hostile world

Arbitrate behavior to serve multiple goals

reflective

deliberative

reactive

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Motivation and Autopoesis

Introduce internal assessment of “well-being”

Critical parameters essential to survival stay within bounded range

Temperature Energy level Etc.

Self-regulatory system tied to survival Flexibility arbitrate the satisfaction of

multiple goals Dynamic prioritization of “needs” Helps to orchestrate other systems

(resources) to address these “needs” Bias attention (saliency) Bias behavior selection (value) Bias form of motor expression (intensity)

Eat

Old pizza at 4am

Awesome Cake after big meal

Hunger

Quality offood

Hunger

Degree of Hunger

RavenousStuffed

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Affect & decision making Promotes better decision making and learning

Emotion theorists – people make poor decisions concerning their welfare without emotions Marvin Minsky’s The Emotion Machine Roz Picard’s Affective Computation

Two complementary systems for systems that must perform tasks in dynamic, unpredictable, and sometimes hostile world.

Cognition interprets and makes sense of the world Affect evaluates and judges

Modulates operating parameters of cognition Negative leads to “depth first” (tunnel vision, increased vigilance) Positive leads to “breadth first” (creativity, increased curiosity)

Provides warning of possible dangers Deeply intertwined! Handle the unexpected problems

Affect introduces another kind of assessment system A value system with respect to the creature Assess whether something is

Good or bad for me? Hospitable or harmful to me? Desirable or undesirable for me, etc?

Sets expectations as to whether something is potentially problematic to guide behavior

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Emotion & decision making

Emotion introduces another kind of self-regulation system Serves of orchestrate other systems to alter goals and their priority

Attention, Memory, Arousal, Behavior & decision making, Learning, etc.

Basic emotions honed for survival When to explore When to persevere or give up When to escape from a dangerous situation When to confront, etc.

But, provides another motivation system not strictly tied to survival Social The more social the species, the more intelligent, emotional, and expressive Humans being the most

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Emotion & Communication with Others

Emotion and its expression serves as a fundamental communication system Makes your behavior more

predictable and explainable by others

Apply their Theory of Mind/folk psychology

Empathy and “feeling felt” Regulatory system of self in the context

of others “ups the ante’ of complexity of

interaction Now, others ‘act’ on you as well Cannot directly manipulate others,

must socially influence Mutually regulatory --- a dance.

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Communicative Affective Intent

Communication through sharedaffective state

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Issues in Learning Something New

Issues for learning systems Knowing what matters Knowing what action to try Evaluating actions Correcting errors Recognize success Structuring learning

For robots, these are addressed in design of learning architecture, algorithm for known task

But what if want to learn something that the system has not been designed to learn?

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Natural Learners

Animals are sensible learners Learn what they ought to learn When they ought to learn it

Learning occurs within an environmental, behavioral, and motivational context

Animals address the issues of Who to learn from? What to learn? Where to learn? When to learn? How to learn? Why learn and for what purpose?

Reflective element to learning processes

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Better Learners, Better Teachers

Learn on its own Constraint from innate endowments

Learn in partnership with person Humans are natural & motivated

teachers Guide exploration to accelerate

learning Rewarding to teach

Sensible attempts given feedback Transparent behavior Learns sufficiently quickly Show eager & interested

View learning and teaching as a coupled system

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Curious machines

Curious machines ground learning in behavioral and motivational context Reflect upon its own learning process Pro-active, self motivated learners Transparent behavior and feedback Leverage from teaching to guide exploration

Persistent Personal Assistant Robot as partner, not tool

reflective

deliberative

reactive

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Principles of biologically inspired design

From insectoids to humanoids, biology inspires Lessons in scaling

managing physical complexity managing behavioral complexity

Design principles Modularity of simpler interacting

systems Internal assessment Self regulation mechanisms

From reactive to deliberative to reflective systems Different mechanisms & systems at each

level Themes hold at multiple levels of

description

reflective

deliberative

reactive

biologically inspired design for (more) scalable robots cba fall 2002 cynthia breazeal mit media lab...

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