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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