1. Artificial Intelligence
Instructor: Monica Nicolescu

2. Artificial Intelligence
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

3. Artificial Intelligence
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
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4. Artificial Intelligence
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
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5. Artificial Intelligence
What is Robotics?
Robotics is the study of robots, autonomous embodied systems interacting with the physical world
A robot is an autonomous system which exists in the physical world, can sense its environment and can act on it to achieve some goals
Robotics addresses perception, interaction and action, in the physical world
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6. Artificial Intelligence
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
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7. Artificial Intelligence
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.?...
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8. Artificial Intelligence
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
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9. 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
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10. An assortment of robots…
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11. Anthropomorphic Robots
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12. Artificial Intelligence
Animal-like Robots
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13. Artificial Intelligence
Humanoid Robots
Asimo (Honda)
QRIO
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DB (ATR)
Robonaut (NASA)
Sony Dream Robot

14. Artificial Intelligence
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

15. A Brief History of Robotics
Robotics grew out of the fields of control theory, cybernetics and AI
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
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16. Artificial Intelligence
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
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17. 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
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18. Artificial Intelligence
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)
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19. Artificial Intelligence
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:
Inhibitory connections
Vehicle 1
Being “ALIVE”
“FEAR” and “AGGRESSION”
Vehicle 2
“LOVE”
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20. 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
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21. Artificial Intelligence
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)
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22. Artificial Intelligence
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

23. Artificial Intelligence
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
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24. Artificial Intelligence
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
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25. Spectrum of robot control
From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998
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26. Artificial Intelligence
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
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27. 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.
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28. Artificial Intelligence
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)
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29. Artificial Intelligence
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
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30. 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
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31. Artificial Intelligence
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)
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32. Artificial Intelligence
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

33. 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
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34. Vertical v. Horizontal Systems
Traditional (SPA):
sense – plan – act
Subsumption:
(Rodney Brooks)
“The world is its own best model.”
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35. 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
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36. Artificial Intelligence
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
level 2
level 1
level 0
sensors
actuators
AFSM
inputs
outputs
suppressor
inhibitor I s
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37. Subsumption Architecture Validation
Practically demonstrated on navigation, 6-legged walking, chasing, soda-can collection, etc.
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38. Artificial Intelligence
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

39. 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
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40. 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
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41. Artificial Intelligence
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

42. 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
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43. Artificial Intelligence
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.)
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44. 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
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45. 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
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46. 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
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47. 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
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48. Example of Behavior Coordination
Fusion: flocking (formations)
Arbitration:  foraging (search, coverage)
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49. Example of representation
A network of behaviors representing spatial landmarks, used for path planning by message-passing (Matarić 90)
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50. 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
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51. Artificial Intelligence
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
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52. Artificial Intelligence
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”
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53. Artificial Intelligence
Background Readings
Ronald Arkin, “Behavior-Based Robotics”, 2001.
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