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Artificial Intelligence Instructor: Monica Nicolescu Outline Introduction Robotics: what it is, what it isn’t, and where it came from Key concepts Brief history Robot control architectures Deliberative control Reactive control Hybrid control Behavior-based control Key Concepts

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artificial intelligence

Artificial Intelligence

Instructor: Monica Nicolescu

outline
Outline
  • Introduction
    • Robotics: what it is, what it isn’t, and where it came from
    • Key concepts
  • Brief history
  • Robot control architectures
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

key concepts
Key Concepts
  • Situatedness
    • Agents are strongly affected by the environment and deal with its immediate demands (not its abstract models) directly
  • Embodiment
    • Agents have bodies, are strongly constrained by those bodies, and experience the world through those bodies, which have a dynamic with the environment

Artificial Intelligence

key concepts cont
Key Concepts (cont.)
  • Situated intelligence
    • is an observed property, not necessarily internal to the agent or to a reasoning engine; instead it results from the dynamics of interaction of the agent and environment
    • and behavior are the result of many interactions within the system and w/ the environment, no central source or attribution is possible

Artificial Intelligence

what is robotics
What is Robotics?
  • Robotics is the study of robots, autonomous embodied systems interacting with the physical world
  • A robot is an autonomoussystem which exists in the physical world, can senseits environment and canacton it to achieve some goals
  • Robotics addresses perception, interaction and action, in the physical world

Artificial Intelligence

uncertainty
Uncertainty
  • Uncertainty is a key property of existence in the physical world
  • Physical sensors provide limited, noisy, and inaccurate information
  • Physical effectors produce limited, noisy, and inaccurate action
  • The uncertainty of physical sensors and effectors is not well characterized, so robots have no available a priori models

Artificial Intelligence

uncertainty cont
Uncertainty (cont.)
  • A robot cannot accurately know the answers to the following:
    • Where am I?
    • Where are my body parts, are they working, what are they doing?
    • What did I just do?
    • What will happen if I do X?
    • Who/what are you, where are you, what are you doing, etc.?...

Artificial Intelligence

the term robot
The term “robot”
  • Karel Capek’s 1921 play RUR (Rossum’s Universal Robots)
  • It is (most likely) a combination of “rabota” (obligatory work) and “robotnik” (serf)
  • Most real-world robots today do perform such “obligatory work” in highly controlled environments
    • Factory automation (car assembly)
  • But that is not what robotics research about; the trends and the future look much more interesting

Artificial Intelligence

classical activity decomposition
Classical activity decomposition
  • Locomotion (moving around, going places)
    • factory delivery, Mars Pathfinder, lawnmowers, vacuum cleaners...
  • Manipulation (handling objects)
    • factory automation, automated surgery...
  • This divides robotics into two basic areas
    • mobile robotics
    • manipulator robotics
  • … but these are merging in domains like robot pets, robot soccer, and humanoids

Artificial Intelligence

an assortment of robots
An assortment of robots…

Artificial Intelligence

anthropomorphic robots
Anthropomorphic Robots

Artificial Intelligence

animal like robots
Animal-like Robots

Artificial Intelligence

humanoid robots
Humanoid Robots

QRIO

Asimo (Honda)

Artificial Intelligence

DB (ATR)

Robonaut (NASA)

Sony Dream Robot

outline14
Outline
  • Introduction
    • Robotics: what it is, what it isn’t, and where it came from
    • Key concepts
  • Brief history
  • Robot control architectures
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

a brief history of robotics
A Brief History of Robotics
  • Robotics grew out of the fields of control theory, cyberneticsandAI
  • Robotics, in the modern sense, can be considered to have started around the time of cybernetics (1940s)
  • Early AI had a strong impact on how it evolved (1950s-1970s), emphasizing reasoning and abstraction, removal from direct situatedness and embodiment
  • In the 1980s a new set of methods was introduced and robots were put back into the physical world

Artificial Intelligence

cybernetics
Cybernetics
  • Pioneered by Norbert Wiener in the 1940s
  • Combines principles of control theory, information science and biology
  • Sought principles common to animals and machines, especially with regards to control and communication
  • Studied the coupling between an organism and its environment

Artificial Intelligence

w grey walter s tortoise
W. Grey Walter’s Tortoise
  • Machina Speculatrix” (1953)
    • 1 photocell, 1 bump sensor, 1 motor, 3 wheels, 1 battery, analog circuits
  • Behaviors:
    • seek light
    • head toward moderate light
    • back from bright light
    • turn and push
    • recharge battery
  • Uses reactive control, with behavior prioritization

Artificial Intelligence

braitenberg vehicles
Braitenberg Vehicles
  • Valentino Braitenberg (1980)
  • Thought experiments
    • Use direct coupling between sensors and motors
    • Simple robots (“vehicles”) produce complex behaviors that appear very animal, life-like
  • Excitatory connection
    • The stronger the sensory input, the stronger the motor output
    • Light sensor  wheel: photophilic robot (loves the light)
  • Inhibitory connection
    • The stronger the sensory input, the weaker the motor output
    • Light sensor  wheel: photophobic robot (afraid of the light)

Artificial Intelligence

example vehicles
Example Vehicles
  • Wide range of vehicles can be designed, by changing the connections and their strength
  • Vehicle 1:
    • One motor, one sensor
  • Vehicle 2:
    • Two motors, two sensors
    • Excitatory connections
  • Vehicle 3:
    • Two motors, two sensors
    • Inhibitory connections

Vehicle 1

Being “ALIVE”

“FEAR” and “AGGRESSION”

Vehicle 2

“LOVE”

Artificial Intelligence

artificial intelligence20
Artificial Intelligence
  • Officially born in 1956 at Dartmouth University
    • Marvin Minsky, John McCarthy, Herbert Simon
  • Intelligence in machines
    • Internal models of the world
    • Search through possible solutions
    • Plan to solve problems
    • Symbolic representation of information
    • Hierarchical system organization
    • Sequential program execution

Artificial Intelligence

ai and robotics
AI and Robotics
  • AI influence to robotics:
    • Knowledge and knowledge representation are central to intelligence
  • Perception and action are more central to robotics
  • New solutions developed: behavior-based systems
    • “Planning is just a way of avoiding figuring out what to do next” (Rodney Brooks, 1987)
  • First robots were mostly influenced by AI (deliberative)

Artificial Intelligence

outline22
Outline
  • Introduction
    • Robotics: what it is, what it isn’t, and where it came from
    • Key concepts
  • Brief history
  • Robot control architectures
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

control architecture
Control Architecture
  • A robot control architecture provides the guiding principles for organizing a robot’s control system
  • It allows the designer to produce the desired overall behavior
  • The term architecture is used similarly as “computer architecture”
    • Set of principles for designing computers from a collection of well-understood building blocks
  • The building-blocks in robotics are dependent on the underlying control architecture

Artificial Intelligence

robot control
Robot Control
  • Robot control is the means by which the sensing and action of a robot are coordinated
  • There are infinitely many ways to program a robot, but there are only few types of robot control:
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

spectrum of robot control
Spectrum of robot control

From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998

Artificial Intelligence

thinking vs acting
Thinking vs. Acting
  • Thinking/Deliberating
    • involves planning (looking into the future) to avoid bad solutions
    • flexible for increasing complexity
    • slow, speed decreases with complexity
    • thinking too long may be dangerous
    • requires (a lot of) accurate information
  • Acting/Reaction
    • fast, regardless of complexity
    • innate/built-in or learned (from looking into the past)
    • limited flexibility for increasing complexity

Artificial Intelligence

robot control approaches
Robot control approaches
  • Reactive Control
    • Don’t think, (re)act.
  • Deliberative (Planner-based) Control
    • Think hard, act later.
  • Hybrid Control
    • Think and act separately & concurrently.
  • Behavior-Based Control (BBC)
    • Think the way you act.

Artificial Intelligence

a brief history
A Brief History
  • Deliberative Control (late 70s)
  • Reactive Control (mid 80s)
    • Subsumption Architecture (Rodney Brooks)
  • Behavior-Based Systems (late 80s)
  • Hybrid Systems (late 80s/early 90s)

Artificial Intelligence

outline29
Outline
  • Introduction
    • Robotics: what it is, what it isn’t, and where it came from
    • Key concepts
  • Brief history
  • Robot control architectures
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

deliberative control think hard then act
Deliberative Control: Think hard, then act!
  • In DC the robot uses all the available sensory information and stored internal knowledge to create a plan of action: sense  plan  act (SPA) paradigm
  • Limitations
    • Planning requires search through potentially all possible plans  these take a long time
    • Requires a world model, which may become outdated
    • Too slow for real-time response
  • Advantages
    • Capable of learning and prediction
    • Finds strategic solutions

Artificial Intelligence

early ai robots
Early AI Robots
  • Shakey (1960, Stanford Research Institute)
  • Stanford Cart (1977) and CMU rover (1983)
  • Interpreting the structure of the environment from visual input involved complex processing and required a lot of deliberation
  • Used state-of-the-art computer vision techniques to provide input to a planner and decide what to do next (how to move)

Artificial Intelligence

outline32
Outline
  • Introduction
    • Robotics: what it is, what it isn’t, and where it came from
    • Key concepts
  • Brief history
  • Robot control architectures
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

reactive control don t think react
Technique for tightly coupling perception and action to provide fast responses to changing, unstructured environments

Collection of stimulus-response rules

Limitations

No/minimal state

No memory

No internal representations

of the world

Unable to plan ahead

Advantages

Very fast and reactive

Powerful method: animals are largely reactive

Reactive Control:Don’t think, react!

Artificial Intelligence

vertical v horizontal systems
Vertical v. Horizontal Systems

Traditional (SPA):

sense – plan – act

Subsumption:

(Rodney Brooks)

“The world is its own best model.”

Artificial Intelligence

the subsumption architecture
The Subsumption Architecture
  • Principles of design
    • systems are built

incrementally

    • components are task-achieving

actions/behaviors (avoid-obstacles, find-doors, visit-rooms)

    • all rules can be executed in parallel, not in a sequence
    • components are organized in layers, from the bottom up
    • lowest layers handle most basic tasks
    • newly added components and layers exploit the existing ones

Artificial Intelligence

subsumption layers

inhibitor

level 2

s

inputs

outputs

level 1

AFSM

I

level 0

suppressor

Subsumption Layers
  • First, we design, implement and debug layer 0
  • Next, we design layer 1
    • When layer 1 is designed, layer 0 is taken into consideration and utilized, its existence is subsumed
    • Layer 0 continues to function
  • Continue designing layers, until the desired task is achieved
  • Higher levels can
    • Inhibit outputs of lower levels
    • Suppress inputs of lower levels

sensors

actuators

Artificial Intelligence

subsumption architecture validation
Subsumption Architecture Validation
  • Practically demonstrated on navigation, 6-legged walking, chasing, soda-can collection, etc.

Artificial Intelligence

outline38
Outline
  • Introduction
    • Robotics: what it is, what it isn’t, and where it came from
    • Key concepts
  • Brief history
  • Robot control architectures
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

hybrid control think and act independently concurrently
Hybrid Control: Think and act independently & concurrently!
  • Combination of reactive and deliberative control
    • Reactive layer (bottom): deals with immediate reaction
    • Deliberative layer (top): creates plans
    • Middle layer: connects the two layers
  • Usually called “three-layer systems”
  • Major challenge: design of the middle layer
    • Reactive and deliberative layers operate on very different time-scales and representations (signals vs. symbols)
    • These layers must operate concurrently
  • Currently one of the two dominant control paradigms in robotics

Artificial Intelligence

reaction deliberation coordination
Reaction – Deliberation Coordination

Flakey

  • Selection:

Planning is viewed as configuration

  • Advising:

Planning is viewed as advice giving

  • Adaptation:

Planning is viewed as adaptation

  • Postponing:

Planning is viewed as a least commitment process

TJ

Artificial Intelligence

outline41
Outline
  • Introduction
    • Robotics: what it is, what it isn’t, and where it came from
    • Key concepts
  • Brief history
  • Robot control architectures
    • Deliberative control
    • Reactive control
    • Hybrid control
    • Behavior-based control

Artificial Intelligence

behavior based control think the way you act
Behavior-Based Control Think the way you act!
  • An alternative to hybrid control, inspired from biology
  • Behavior-based control involves the use of “behaviors” as modules for control
  • Historically grew out of reactive systems, but not constrained
  • Has the same expressiveness properties as hybrid control
  • The key difference is in the “deliberative” component

Artificial Intelligence

what is a behavior
What Is a Behavior?

Rules of implementation

  • Behaviors achieve or maintain particular goals (homing, wall-following)
  • Behaviors are time-extended processes
  • Behaviors take inputs from sensors and from other behaviors and send outputs to actuators and other behaviors
  • Behaviors are more complex than actions (stop, turn-right vs. follow-target, hide-from-light, find-mate etc.)

Artificial Intelligence

principles of bbc design
Principles of BBC Design
  • Behaviors are executed in parallel, concurrently
    • Ability to react in real-time
  • Networks of behaviors can store state (history), construct world models/representation and look into the future
    • Use representations to generate efficient behavior
  • Behaviors operate on compatible time-scales
    • Ability to use a uniform structure and representation throughout the system

Artificial Intelligence

behavior coordination
Behavior Coordination
  • Behavior-based systems require consistent coordination between the component behaviors for conflict resolution
  • Coordination of behaviors can be:
    • Competitive: one behavior’s output is selected from multiple candidates
    • Cooperative: blend the output of multiple behaviors
    • Combination of the above two

Artificial Intelligence

competitive coordination
Competitive Coordination
  • Arbitration: winner-take-all strategy  only one response chosen
  • Behavioral prioritization
    • Subsumption Architecture
  • Action selection/activation spreading (Pattie Maes)
    • Behaviors actively compete with each other
    • Each behavior has an activation level driven by the robot’s goals and sensory information
  • Voting strategies
    • Behaviors cast votes on potential responses

Artificial Intelligence

cooperative coordination
Cooperative Coordination
  • Fusion: concurrently use the output of multiple behaviors
  • Major difficulty in finding a uniform command representation amenable to fusion
  • Fuzzy methods
  • Formal methods
    • Potential fields
    • Motor schemas
    • Dynamical systems

Artificial Intelligence

example of behavior coordination
Example of Behavior Coordination

Fusion: flocking (formations)

Arbitration:  foraging (search, coverage)

Artificial Intelligence

example of representation
Example of representation
  • A network of behaviors representing spatial landmarks, used for path planning by message-passing (Matarić 90)

Artificial Intelligence

behavior based control summary
Behavior-Based Control summary
  • Alternative to hybrid systems; encourages uniform time-scale and representation throughout the system
  • Scalable and robust
  • Behaviors are reusable; behavior libraries
  • Facilitates learning
  • Requires a clever means of distributing representation and any potentially time-extended computation

Artificial Intelligence

robotics challenges
Robotics Challenges
  • Perception
    • Limited, noisy sensors
  • Actuation
    • Limited capabilities of robot effectors
  • Thinking
    • Time consuming in large state spaces
  • Environments
    • Dynamic, impose fast reaction times

Artificial Intelligence

lessons learned
Lessons Learned
  • Move faster, more robustly
  • Think in such a way as to allow this action
  • New types of robot control:
    • Reactive, hybrid, behavior-based
  • Control theory
    • Continues to thrive in numerous applications
  • Cybernetics
    • Biologically inspired robot control
  • AI
    • Non-physical, “disembodied thinking”

Artificial Intelligence

background readings
Background Readings
  • Ronald Arkin, “Behavior-Based Robotics”, 2001.

Artificial Intelligence