Topics: Introduction to Robotics CS 491691X

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Topics Introduction to RoboticsCS 491/691(X)

• Lecture 10
• Instructor Monica Nicolescu

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Review

• The Subsumption Architecture
• Herbert, Genghis, The Nerd Herd, Tom and Jerry
• Advantages and dissadvantages
• Behavior-based control
• Definitions
• Principles of behavior-based control
• Toto a behavior-based mapping robot

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An Example Task Mapping

• Design a robot that is capable of
• Moving around safely
• Make a map of the environment
• Use the map to find the shortest paths to
  particular places
• Navigation mapping are the most common mobile
  robot tasks

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Map Representation

• The map is distributed over different behaviors
• We connect parts of the map that are adjacent in
  the environment by connecting the behaviors that
  represent them
• The network of behaviors represents a network of
  locations in the environment
• Topological map Toto (Mataric 90)

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Totos Behaviors

• Toto the robot
• Ring of 12 sonars, low-resolution compas
• Lowest level move around safely, without
  collisions
• Next level following boundaries, a behavior that
  keeps the robot near walls and other objects
• Landmark detection keep track of what was sensed
  and how it was moving
• meandering ? cluttered area
• constant compass direction, go straight ? left,
  right walls
• moving straight, both walls ? corridor

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Totos Controller
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Building a Map

• Each landmark was stored in a behavior
• Whenever a new landmark was discovered a new
  behavior was added
• Adjacent landmarks are connected by communication
  wires
• This resulted in a topological representation of
  the environment, i.e., a topological world model

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Totos Mapping Behaviors

• Each landmark was stored in a behavior
• Each such landmark behavior stored (remembered)
  some information
• landmark type (wall, corridor, irregular)
• compass heading
• approximate length/size
• some odometry

• my-behavior-type corridor
• my-compass-direction NV
• my-approximate-location x,y
• my-approximate-length length
• whenever received (input)
• if input(behavior-type) my-behavior-type
• and input (compass-direction)
  my-compass-direction
• then
• active lt- true

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Localization

• Whenever a landmark is detected, its description
  (type, compass direction, etc.) is sent to all
  behaviors in parallel ? the one that matches
  becomes active
• When nothing in the map matches ? a new
  place/landmark was discovered and added to the
  map
• If an existing behavior was activated, it
  inhibited any other active behaviors

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Getting Around

• Toto can use the map to navigate
• The behavior that corresponds to the goal sends
  messages (spreads activation) to all of its
  neighbors
• The neighbors send messages to their neighbors in
  turn
• So on, until the messages reach the currently
  active behavior
• This forms path(s) from the current state to the
  goal

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Path Following

• Toto did not keep a sequence of behaviors
• Instead, messages were passed continuously
• Useful in changing environments
• How does Toto decide where to go if multiple
  choices are available?
• Rely on the landmark size behaviors add up their
  own length as messages are passed from one
  behavior to another ? give path length
• Choose the shortest path
• Thus, one behavior at a time, it reached the goal

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Toto - Video
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Expression of Behaviors

• Example
• Going from a classroom to another
• What does it involve?
• Getting to the destination from the current
  location
• Not bumping into obstacles along the way
• Making your way around students on corridors
• Deferring to elders
• Coping with change in the environment

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Stimulus-Response Diagrams

• Behaviors are represented as a general response
  to a particular stimulus

C O O R D I N A T O R
move-to-class
class location
avoid-object
detected object
dodge-student
Actions
detected student
stay-right
detected path
defer-to-elder
detected elder
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Finite State Acceptor Diagrams

• Useful for describing aggregations and sequences
  of behaviors
• Finite state acceptor
• Q set of allowable behavioral states
• ? transition function from (input, current
  state) ? new state
• q0 initial state
• F set of accepting (final) states

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Formal Methods

• Have very useful properties for the programmer
• Can be used to verify designer intentions
• Facilitate the automatic generation of control
  systems
• Provide a common language for expressing robot
  behaviors
• Provide a framework for formal analysis of the
  program, adequacy and completeness
• Provide support for high-level programming
  language design

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Situated Automata

• Kaelbling Rosenschein (91)
• Controllers are designed as logic circuits
• Capable of reasoning
• REX Lisp-based language
• First language to encode a reactive systems with
  synchronous digital circuitry
• Gapps goals are specified more directly
• Achievement, maintenance, execution
• Logical boolean operators are used to create
  higher-level goals ? generate circuits

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Situated Automata

• (defgoalr (ach in-classroom)
• (if (not start-up)
• (maint (and (maint move-to-classroom)
  (maint avoid-objects)
• (maint dodge-students)
• (maint stay-to-right-on-path)
• (maint defer-to-elders)
• )
• )
• )
• )

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Discrete Behavioral Encoding

• The behavior consists of a set finite set of
  (situation, response) pairs
• Rule-based systems
• IF antecedent THEN consequent
• Condition-action production rules (Nilsson 94)
• Produce durative actions move at 5m/s vs.
  move 5 m
• The Subsumption Architecture
• (whenever condition consequent)

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Continuous Behavioral Encoding

• Continuous response provides a robot an infinite
  space of potential reactions to the world
• A mathematical function transforms the sensory
  input into a behavioral reaction
• Potential fields
• Law of universal gravitation potential force
  drops off with the square of the distance between
  objects
• Goals are attractors and obstacles are repulsors
• Separate fields are used for each object
• Fields are combined (superposition) ? unique
  global field

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Potential Fields
Ballistic goal attraction field
Superposition of two fields
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Potential Fields

• Advantages
• Provide an infinite set of possibilities of
  reaction
• Highly parallelizable
• Disadvantages
• Local minima, cyclic-oscillatory behavior
• Apparently, large amount of time required to
  compute the entire field reaction is computed
  only at the robots position!

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Motor Schemas

• Motor schemas are a type of behavior encoding
• Based on neuroscience and cognitive science
• They are based on schema theory (Arbib)
• Explains motor behavior in terms of the
  concurrent control of many different activities
• Schemas store how to react and the way the
  reaction can be realized basic units of activity
• Schema theory provides a formal language for
  connecting action and perception
• Activation levels are associated with schemas,
  and determine their applicability for acting

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Visually Guided Behaviors

• Michael Arbib colleagues constructed computer
  models of visually guided behaviors in frogs and
  toads
• Toads frogs respond visually to
• Small moving objects ? feeding behavior
• Large moving objects ? fleeing behavior
• Behaviors implemented as a vector field (schemas)
• Attractive force (vector) along the direction of
  the fly
• What happens when presented with two files
  simultaneously?
• The frog sums up the two vectors and snaps
  between the two files, missing both of them

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Motor Schemas

• Provide large grain modularity
• Schemas act concurrently, in a cooperative but
  competing manner
• Schemas are primitives from which more complex
  behaviors (assemblages can be constructed)
• Represented as vector fields

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Examples of Schemas

• Obstacle avoid and stay on corridor schemas

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Schema Representation

• Responses represented in uniform vector format
• Combination through cooperative coordination via
  vector summation
• No predefined schema hierarchy
• Arbitration is not used
• each behavior has its contribution to the robots
  overall response
• gain values control behavioral strengths
• Here is how

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The Role of Gains in Schemas

• Low gain

• High gain

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Designing with Schemas

• Characterize motor behaviors needed
• Decompose to most primitive level, use biological
  guidelines where appropriate
• Develop formulas to express reaction
• Conduct simple simulations
• Determine perceptual needs to satisfy motor
  schema inputs
• Design specific perceptual algorithms
• Integrate/test/evaluate/iterate

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Foraging Example
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Strengths and Weaknesses

• Strengths
• support for parallelism
• run-time flexibility
• timeliness for development
• support for modularity
• Weaknesses
• hardware retargetability
• combination pitfalls (local minima, oscillations)

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Schema-Based Robots

• At Georgia Tech (Ron Arkin)
• Exploration
• Hall following
• Wall following
• Impatient waiting
• Navigation
• Docking
• Escape
• Forage

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The DAMN Architecture

• Distributed Architecture for Mobile Navigation
  (Rosenblatt 1995)
• Multi-valued behaviors (at all levels) propose
  multiple action preferences
• Each behavior votes for or against sets of
  actions
• Arbiter selects max weighted vote sum
• Practically demonstrated on real-world
  long-distance navigation
• Disadvantage highly heuristic

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Behavior Coordination

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

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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 robots goals and sensory information
• Voting strategies (DAMN)
• Behaviors cast votes on potential responses

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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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Emergent Behavior

• The resulting robot behavior may sometimes be
  surprising or unexpected ? emergent behavior
• Emergence arises from
• A robots interaction with the environment
• The interaction of behaviors

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Wall Following

• A simple wall following controller
• If too close on the left, turn right
• If too close on the right, turn left
• Otherwise, keep straight
• If the robot is placed close to a wall it will
  follow
• Is this emergent?
• The robot has no explicit representations of
  walls
• The controller does not specify anything explicit
  about following

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Emergence

• A holistic property, where the behavior of the
  robot is greater than the sum of its parts
• A property of a collection of interacting
  components
• Often occurs in reactive and behavior-based
  systems (BBS)
• Typically exploited in reactive and BBS design

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Flocking

• How would you design a flocking behavior for a
  group of robots?
• Each robot can be programmed with the same
  behaviors
• Dont get too close to other robots
• Dont get too far from other robots
• Keep moving if you can
• When run in parallel these rules will result in
  the group of robots flocking

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Readings

• M. Mataric Chapters 17, 18