Biologically Inspired RobotsRobert Stengel

PSY 214, Human Identity in the Age of Neuroscience and

Information Technology2006

Robotics• Design, manufacture,

control, and programming ofrobots

• Use of robots to solveproblems

• Study of control processes,sensors, and algorithmsused in humans, animals,and machines

• Application of controlprocesses and algorithms todesigning robots

Intelligent Agents

• Perform useful functions driven by desiredgoals and current knowledge

– Emulate biological and cognitive processes

– Process information to achieve objectives

– Learn by example or from experience

– Adapt functions to a changing environment

Robots inScience Fiction

• Victorian pulp fiction (FrankReade’s Electric Man, 1886)

• “Robot” = “worker” in Slavic

– Karel Capek’s 1921 play, RUR(Rossum’s Universal Robots), inwhich machines took over theworld

• Elektro, 1939 NY World’s Fair

• I Robot, short story, Isaac

Asimov, 1950

– Code of ethics for robots

• Do no harm

• Obey orders from humans that

don’t violate #1

• Protect own existence if it does

not conflict with #1 or #2

Applications of Robotics

• 1940s: World War II– Teleoperators

– Fire control systems

– Aerial drones

– Numerically controlled machines

– Chemical process control

• 1950s: Cold War– Industrial robots

• 1960s: Space Age– Medical prosthetics

– Uninhabited spacecraft

– Robot kits (e.g., Heathkit)

Intelligent Agents inScience Fiction

• The creation of “life”

– Pygmalion and Galatea, Greekmythology

– “The creature” inFrankenstein, 1818, by MaryShelley

• HAL in 2001: A SpaceOdyssey (1968)

• Droids and Bots in Star Wars(1977)

Applications ofIntelligent Agents

• Eliza [http://www-ai.ijs.si/eliza/eliza.html]

• Statistical decision theory

• Symbolic computation

• Voice response systems

• Yahoo, Google

• WORD’s “Paper Clip”

Superheroes, Androids,Gynoids, and Cyborgs

• Bionic man and woman

• Superman • Androids

• Gynoids

• Cyborgs

Wearable Computers

EyeBud

BlueTooth Headset

MIThril

iPod

Automatons

Pirates of the CaribbeanRepliee Q1

Cognitive and Biological

Paradigms for Intelligent Agents

Thinking- Syntax (form) and Semantics (meaning)- Algorithmic vs. Non-Algorithmic Behavior

- Consistency, Emotion, "The Collective Subconscious"- Generating Alternatives- Randomized Search

Consciousness- Self-Awareness and Perception

- Creativity, Wisdom, and Imagination- Common Sense, Understanding, and Judgment of Truth- Learning by Example

Recognizing Aliases and Objects

Handling EmergenciesFocusing on Things that are "Out of Focus"Richness of Sensory InformationHierarchical and Redundant StructuresGenetic Reproduction of Elements

Learning Requires Error or Incompleteness

Biological Adaptation is a Slow Process

Rest is an Essential Feature of Intelligent Biological Systems

REM (Rapid Eye Movement) Sleep is a Time of Learning,

Consolidating, and Pruning Knowledge

Cells Undergo Birth-Life-Death Cycle

Central Nervous System Does Not Regenerate

Short-Term Memory Recedes into Long-Term Memory or is

Forgotten

Humans Form Chords of Actions

Knowledge Acquisition,

Behavior, Aging, and Control

Richness of Sensory Information

Short, Dedicated, Parallel Channels for High-

Bandwidth, High-Resolution Information (vision,sound, and balance)

Dissimilar but Related Sensory Inputs

Hierarchical, Redundant, and Adaptive Structures

Pairing Allows Graceful Degradation of Sensors and

Effectors

Biological Paradigms for

Intelligent Control

Conscious Thought- Awareness- Focus- Reflection

- Rehearsal- Declarative Processing of Knowledge or

Beliefs

Unconscious Thought

- Subconscious Thought> Procedural Processing> Communication> Learned Skills> Subliminal Knowledge Acquisition

- Preconscious Thought> Pre-attentive Declarative Processing> Subject Selection for Conscious Thought> Concept Development> Information Pathway to Memory

> Intuition

Hierarchy of

Declarative, Procedural,and Reflexive Actions

Reflexive Behavior- Instantaneous Response to

Stimuli- Elementary, Forceful Actions- Stabilizing Influence- Simple Goals

Intelligent Agent PossessingDeclarative, Procedural, and Reflexive Traits

Declarative Functions Expert Systems, Decision TreesProcedural Functions modeled by Estimation and Control "Circuits"Reflexive Functions Neural Networks

Hierarchical Feedback Control

• Inner Loop

– Small Amplitude

– Fast Response

– High Bandwidth

• Middle Loop

– Moderate Amplitude

– Medium Response

– Moderate Bandwidth

• Outer Loop

– Large Amplitude

– Slow Response

– Low Bandwidth

• Feedback

– Error between command andfeedback signal drives nextinner-most loop

Human Joints

Shoulder

Elbow

Wrist/Hand

Hand/Fingers

Skeleton and Muscle-

Induced Motion

RoboticJoints

• Revolute Joints: Rotation about a single

axis

– Parallel to Link

– Perpendicular to Link

• Prismatic Joints: Sliding

along a single axis

• Other Joint Types– Cylindrical (sliding and

turning)

– Screw (helical motion)

– Flexible

– Spherical (or ball)

– Planar (sliding on a plane)

Robotic Actuators and Linkages

Two-Link/Three-Joint

Manipulator

Assignment of

coordinate framesWorkspace

End Effectors

• Multi-digit hand

• Gripper, clamp

• Machine tools

– Grinding, sanding

– Inserting screws

– Drilling

– Hammering

• Paint sprayer

Prosthetic Arm

Jesse Sullivan, Rehabilitation Institute of Chicago

Biologic Sensing

• Proprioception

– Position and posture of limbs andbody

• Tactile sensation

• Pain

• Temperature

• Differentiated neuronal receptors

Robotic Sensing Walking Robots• Troody• Flamingo

• Planar Man

Smart Knee

• Stairs (Traditional Prosthetic) • Stairs (Smart Knee)

Robotic Exoskeleton

Hard and Soft Thinking

• Problem-solving approaches– Logical / Metaphorical

– Reasonable / Dream-like

– Serious / Humorous

– Definite / Ambiguous

– Consistent / Paradoxical

– Laborious / Playful

– Exact / Approximate

– Real / Fantastic

– Focused / Diffuse

– Analytical / Illogical

– Specific / General

– Mature / Immature

• Crisp / Fuzzy

Fuzzy Experiment

• What does eachterm mean?

• Normalize results so that the maximum is 1

• Normalized plots are fuzzy membershipfunctions

A few?

Several?

A lot?

Linguistic FuzzyControl System

• Antecedent and consequent are both fuzzy propositions

– e.g., “If error is small and error rate is negative, then controlcommand is small”

– What are “small”, “medium”, and “large”?

• Must “fuzzify” physical error/rate, apply fuzzy rules, and “de-fuzzify” control command

Intelligent Systems

• Perform useful functions driven by desiredgoals and current knowledge

– Emulate biological and cognitive processes

– Process information to achieve objectives

– Learn by example or from experience

– Adapt functions to a changing environment

Expert Systems

• Knowledge representation

• Inference or reasoning method(Deduction)

• Knowledge acquisition (Induction)

• Explanation (Abduction)

• User interface

Functions ofExpert Systems

• Design

• Diagnosis

• Instruction

• Interpretation

• Monitoring

• Negotiation

• Planning

• Prediction

• Reconfiguration

• Control/Regulation

Graphical

Representation of

Knowledge:

PrincipledNegotiation

• Flow chart

• Getting to Yes,Fisher and Ury, 1981– Separate the agents

from the problem

– Focus on interests,not positions

– Invent options formutual gain

– Insist on usingobjective criteria

Intelligent Systems

• Perform useful functions driven by

desired goals and current knowledge

– Emulate biological and cognitive processes

– Process information to achieve objectives

– Learn by example or from experience

– Adapt functions to a changing environment

Feedback andAdaptation

• Feedback: Learn thecurrent dynamic stateof the system andadjust the controlcommands

• Adaptation: Learn thecurrent status of thesystem’s dynamicmodel and adjust thecontrol law

Search

• Typical AI text problems

– Prove a theorem

– Solve a puzzle (e.g., Tower ofHanoi)

– Find a sequence of moves thatwins a game (e.g., chess)

– Find the shortest path (e.g.,Traveling salesman problem)

– Find a sequence of symbolictransformations (e.g.,Mathematica)

• The common thread: search

– Structures for search

– Strategies for search

Reinforcement Learning• Learn from success and failure

• Repetitive trials

– Reward correct behavior

– Penalize incorrect behavior

• Learn to control from a humanoperator

• Q Learning

– Q is an incremental cost function

– Control strategy or parameters areadjusted to reduce Q

Computational (Artificial)Neuron Complex

• Synapse effects represented by weights,gains, or multipliers

• Neuron firing frequency is modeled by lineargain or nonlinear element

w11

w12

w13

w21

w22

w23

Layout of aNeural Network

Layered, parallelstructure for computation

Neural NetworkControl System

y*

+

-

C H

Systemu(t) x(t)

++

- -

-+C

C

C

u(t).

HxuI

F

C

B

• Define an acceptable

“classical” controller

• Design controllers that

satisfy control requirements

at n operating points

• Train neural networks off-line

to replicate control response

at n operating points

• Train on-line to optimize

performance

Task Planning

• Accomplish an objective

– Make a decision

– Gather information

– Build something

– Analyze something

– Destroy something

• Determine and follow a path

– Minimize time or cost

– Take the shortest path

– Avoid obstacles or hazards

• Work toward a common goal

– Integrate behavior with higher objectives

– Do not impede other agents

• Provide leadership for other

agents

– Issue commands

– Receive and decode

information

• Provide assistance to other

agents

– Coordinate actions

– Respond to requests

• Defeat opposing agents

– Compete and win

SubsumptionArchitecture

• Key theses

– Intelligent behavior can be

generated without explicitrepresentations orreasoning

– Intelligent behavior is an

emergent property of certaincomplex systems

– Situation -> action

• Emergent robots

– Genghis

– TuteBot

– RugWarrior

– Roomba

Comparison of Agent Paradigms• Cooperation vs. inhibition

• Assumptions about lower-levelfunctions

• Level-dependent bandwidth

• Explicit vs. implicit decision making

• Lack of redundancy

Are Intelligent RobotsFact or Fiction?

• For further information:– MAE 345, Robotics and Intelligent

Systems

– Certificate Program in Robotics andIntelligent Systems