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Brooks and Subsumption

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Title: Brooks and Subsumption


1
  • Brooks and Subsumption

2
Intelligent Robot Systems
Knowledge
AI control
Perception
Planning and control
Supervision
Basic level control
Sensors
Actuators
World
3
Typical Robot System
world
human
Sensors
Actuators
Human interface
Robot
Execution of program
Control
Data acquisition
4
Tradition approach
  • decomposition by function
  • Sensors ?
    ? Actuators

5
Traditional AI model
R. Brooks, Cambrian Intelligence
6
Supervision/Control of a robot
Human Interface
Control of high levels
Supervision
Control of low level
Control of low levels
Robot
7
Hierarchical Architecture
8
Hierachical Architecture
StrategicThis level generates goals and
strategies for achieving those goals which result
in the achievement of an overall objective
Where to go
TacticalThis level generates the tactics by
which a goal is to be achieved
how
ExecutiveThis level execute the tactical plan
execute
Three Level Architecture
9
Context
  • Physical symbol system hypothesis
  • PSS Has physical patterns called symbols and
    collections of these symbols called expressions
  • PSSH A PSS has the necessary sufficient means
    for general intelligent action
  • Important assumption
  • Intelligence occurs via reasoning
  • E.g., searching for operators logical inference
  • Thinking vs. acting
  • PSSH type approaches emphasize thinking
  • But what about intelligence based on acting?
  • E.g., some nonhuman animals may act more than
    think but they seem to have a kind of
    intelligence

10
Reactive Robotics
The Brooks School
Behavioral Robotics
  • Subsumption architectures for walking machines

Rodney Brooks, Connell
Brooks, R. A. (1999). Cambrian Intelligence The
Early History of the New AI. Cambridge, MA MIT
Press.
11
Who is Rodney Brooks?
  • Adelaide born. Flinders, Stanford, , MIT
  • Fujitsu Professor of Computer Science and
    Engineering (EECS Dept) at MIT.
  • Director of the Artificial Intelligence
    Laboratory at MIT.
  • Companies Lucid, IS Robotics Inc., Artificial
    Creatures.
  • Claims he is a pragmatist.

12
The early years of subsumption
  • Aware of the failure of mobile robotics to live
    up to its potential.
  • Autonomous vehicles were not that autonomous and
    weren't even very good vehicles.
  • Brooks identified various aspects of mobile
    robotics which he considered to be important and
    obvious

13
Key Topics of Behaviour-based Approach
  • Situatedness
  • Embodiment
  • (animal or insect) Intelligence
  • Emergence

14
Situatedness
  • A situated automaton is a finite-state machine
  • whose inputs are provided by sensors connected to
    the environment, and
  • whose outputs are connected to effectors.
  • The world is its own best model
  • Traditional AI, working in symbolic abstracted
    domain.
  • Problem solvers which are not participating in
    the world as agents.
  • Dealing with model world - no real connection to
    external world.

15
Why situatedness is better for mobile robot
behaviors?
  • Alternative approach, to use a mobile robot which
    uses the world as its own model, referring to
    information from sensors rather than internal
    world model.
  • Representations are developed which capture
    relationships of entities to robot.
  • Situated agent must respond in timely fashion to
    inputs but much information from the world.

16
Embodiment
  • Embodiment Physical grounding of robot in real
    world.
  • According to Brooks (1991), embodiment is
    critical for two reasons.
  • Only an embodied agent is validated as one that
    can deal with real world.
  • Only through a physical grounding can any
    internal symbolic system be given meaning.

17
Emergence
  • Intelligence emerges from interaction of
    components of the system.
  • Behaviour-based approach - intelligence emerges
    from interaction of simple modules.
  • e.g. Obstacle avoidance, goal finding, wall
    following modules.

18
Main Ideas of Behavior-based Approach
  • Main ideas
  • No central model maintained of world
  • No central locus of control
  • No separation into perceptual system, central
    system and actuation system
  • Behavioural competence improved by adding one
    more behaviour specific network to existing
    network.
  • Crude analogy to evolutionary development
  • No hierarchical development
  • Layers or behaviors run in parallel

19
Model of Brooks the perceptual and action
subsystems are all there really is
Cognition is only in the eye of an observer
A. Brooks, Cambrian Intelligence
20
Brooks Criticizes traditional robotics
  • Intelligence is determined by the dynamics of
    interaction with the world he says
  • Some activities we think of as intelligent have
    only been taking place for a small fraction of
    our evolutionary lineage.
  • Simple behaviors to do with perception and
    mobility took much longer to evolve.
  • Would make sense to begin by looking at simpler
    animals.
  • - looking at dynamics of interaction of robot
    with its environment.

21
Example of Criticisms of traditional approach -
Brooks
  • We should take evidence from biology and
    evolution.
  • SPA systems highly constrained.
  • Early work formal systems, Blocks World.
  • Funding forced relevance and new slogan.
  • But this ignores knowledge acquisition!
  • Introspection is misleading.
  • Brooks rejects symbol system hypothesis.

22
Key Brooksian Ideas
  • Situatedness and embodiment, as discussed.
  • Approximate evolution
  • Incremental additions improve performance
  • Layers
  • Each layer
  • Corresponds to new behavior
  • Relies upon existing layers
  • Has minimal interaction with other layers
  • Is short connection between perception
    actuation
  • Advantages of this paradigm.

23
Brook's General Robot Requirements
  • He identified a number of requirements of a
    control system for an intelligent autonomous
    mobile robot.
  • Multiple Goals
  • Some conflict, context dependent
  • Multiple Sensors
  • All have errors, inconsistencies and
    contradiction.
  • Robustness
  • The robot must by fault-tolerant.
  • Extensible
  • You have to be able to build on whatever you built

24
Subsumption architecture - the Brooks approach
  • levels of competence
  • example competences
  • layers of control
  • subsumption
  • structure of layers
  • extensions
  • finite state machines

25
What were the weaknesse of traditional approaches?
  • Cant account for large aspects of
    Intelligence
  • Reliant on representation
  • Rapidly changing boundary conditions
  • Hard to map sensor values to physical
    quantities
  • Not robust
  • Relatively slow response
  • Hard to extend
  • Hard to test

26
Brooks Dogma
  • Brooks also introduced, what he called,
  • "9 dogmatic principles",
  • 1) Complex (and useful) behavior need not
    necessarily be a product of an extremely complex
    control system.
  • 2) Things should be simple Interfaces to
    subsystems etc.
  • 3) Build cheap robots that work in human
    environments
  • 4) The world is three-dimensional therefore a
    robot must model the world in 3 dimensions.

27
Dogma (cont)
  • 5) Absolute coordinate systems for a robot are
    the source of large cumulative errors.
  • 6) The worlds where mobile robots will do useful
    work are not constructed of exact simple
    polyhedra.
  • 7) Visual data is useful for high level tasks.
    Sonar may only be good for low level tasks where
    rich environmental descriptions are unnecessary.
  • 8) The robot must be able to perform when one or
    more of its sensors fails or starts giving
    erroneous readings.

28
Dogma (cont)
  • 9) "We are interested in building "artificial
    beings"
  • --robots that survive for days, weeks and months,
  • without human assistance,
  • in a dynamic complex environment.
  • Such robots must be self-sustaining

29
Solution Subsumption
  • Brooks and his group eventually came up with a
    computational architecture.
  • Model arrived at by continually refining attempts
    to program a robot to reactively avoid collisions
    in a people-populated environment.
  • Not intended as a realistic model of how
    neurological systems work.
  • The model is called "subsumption architecture.
  • Its purpose is to program
  • intelligent,
  • situated,
  • embodied agents.

30
new architecturesubsumption
  • Introduced in Brooks seminal 1986 paper
  • Consists of layered behaviors,
  • from simple to complex,
  • with simple interfaces
  • Layers can override each other
  • Each layer has a control program that is capable
    of working at the speed of environmental change
  • Each layer now can do
  • the appropriate model building,
  • sensor fusion,
  • etc.
  • 1. No central model of world.
  • 3. No separation into perception, central
    processing, and actuation.
  • 3. Layering increases capabilities.
  • 4. No hierarchical arrangement.
  • 5. Messages on input ports when needed.
  • 6. Behaviors run in parallel.

31
Basic Architecture of an autonomous agent
Sensors
Cognition
Effectors
Vision, range, touch Decides
actions and Carry out actions

commands
effectors
Subsumption Architecture
  • Control Problem for an autonomous robot
  • Traditional Approach
  • functional decomposition
  • A new approach
  • decomposition by activity

32
Motivation
  • To achieve cooperation and coordination among
    different robots and self governance with human
    like performance in a common work space.

33
Key Problems for Subsumption-based mobile robots.
  • Cooperation
  • Coordination
  • Learning
  • Vision
  • Localization

34
Subsumption Architecture
  • 1. Reactive robotics approach, which Brooks calls
    behavioral robotics.
  • 2. The central idea of Brooks approach is that
    more sophisticated robot competencies should be
    built on top of simpler ones.
  • Instead of all robot inputs feeding into a
    sensory perception unit, which creates a world
    model of the robots environment, which feeds
    into a planning module, Brooks has argued that
  • robot perception and action should be closely
    linked,
  • complex behaviors can be built from the
    interactions of simple ones.
  • Example
  • a robot that must walk should first learn to
    stand.
  • Then later behaviors can exploit earlier ones
    a task which causes a legged robot to move its
    legs can make use of the knowledge embedded in
    the behavior that allows the robot to simply
    stand.

35
Subsumption Architecture Massiveness
  • Brooks has proposed that future unmanned
    interplanetary missions should be performed by
    hundredsor thousandsof simple, insect-like
    robots that act in teams to accomplish work,
    rather than a large and complicated monolithic
    device.
  • Individual robots could be considered expendable
    without jeopardizing the success of the entire
    mission, whereas if a single large robot had a
    failure, the mission would be over.
  • Homework (not this year)
  • Read
  • Rodney Brookss paper on Fast, Cheap, and Out of
    Control

36
Behavior-based Robotics in 1984
  • Groups at MIT and SRI independently began
    rethinking how to organise intelligence (around
    1984).
  • Requirements
  • Reactive to dynamic environment
  • Operate on human time scales
  • Robustness to uncertainty/unpredictability
  • They implemented simple systems with similar
    features.

37
Subsumption Priciples Network Construction
  • 1) Computation is organized as an asynchronous
    network of active computational elements
  • they are augmented finite state machines,
  • with a fixed topology of unidirectional
    connections.
  • 2) Messages sent over connections have no
    implicit semantics
  • they are small numbers (typically 8 or 16 bits,
    but on some robots just 1 bit),
  • their meanings are dependent on the dynamics
    designed into both the sender and receiver
  • 3) Sensors and actuators are connected to this
    network,
  • usually through asynchronous two-sided buffers.

38
Subsumption Architecture Incrementally build
network of state machines
  • Incremental method for building robots
  • Network of finite state machines links sensors to
    actions
  • Internal timers
  • Control system built in layers
  • Model Message passing augmented finite state
    machines

39
Finite State Machines
  • In Brooks original work, each module was
    implemented as a finite state machine
  • augmented with some instance variables
  • A finite state machine has
  • a set of states
  • a start state (one or more stop states)
  • a set of symbols
  • a set of transitions
  • For example
  • an FSM for a light switch
  • An FSM can be implemented in C
  • using a state variable and switch statement

40
Let us look again to more limitations of
Conventional Approaches
  • Perception takes too long
  • Perception is not a solved problem, nor will it
    be solved in the near future.
  • Modeling/planning component assumes complete
    models are available
  • Overall system cannot respond in real-time
  • Most robots built this way have failed
  • (or run very slowly)

41
And the response of the Behavior-based Approach
  • Decompose overall control system into a layered
    set of reactive behaviors
  • Each behavior represents a complete mapping from
    sensors to motor commands
  • Low-level behaviors (e.g. avoid) can run in
    real-time since they use little computation
  • High-level behaviors are invoked only when
    necessary
  • Requires arbitration strategy to choose among
    (or combine) conflicting behaviors

42
What does it mean that behaviors run in parallel
and control directly?
  • The control system is broken down into horizontal
    modules, or behaviors, that run in parallel
  • each behavior has direct access to sensor
    readings and can control the robots motors
    directly

sense
43
Brooks Assumptions for mobile robots Complex
behaviors from simple controls
  • Complex and useful behavior need not necessarily
    be a product of an extremely complex control
    system
  • Things should be simple
  • if a single module is getting too big rethink
    the design
  • unstable or ill-conditioned solutions are not
    good
  • Ability to wander in real environments is crucial
  • The real world is three dimensional
  • the robot must model its environment in 3D
  • Absolute coordinate systems are a source of error
  • relational maps are more useful to a mobile robot

44
Brooks Assumptions for mobile robots sensors
integration and autonomy -
  • The real world is not constructed of exact shapes
  • models may be useful, but should not be used
    alone
  • Sonar data, while easy to collect, do not lead to
    rich descriptions of the world
  • visual data is much better
  • For robustness, the robot must be able to
    function when one or more of its sensors fails or
    starts giving erroneous readings
  • recovery should be quick (self-calibrating)
  • Robots should be autonomous and self-sustaining
    (able to survive weeks without human assistance)

45
Robot Architecture
how much / how do we represent the world
internally ?
Motor Schemas
Just what we need.
Subsumption paradigm
  • Subsumption composes simple reactions
    (behaviors) by letting one take control at an
    appropriate time.
  • World state is computed in order to perform a
    specific task.

46
Larger example -- Genghis
1) Standing by tuning the parameters of two
behaviors the leg swing and the leg
lift 2) Simple walking one leg at a time 3)
Force Balancing incorporated force sensors on
the legs 4) Obstacle traversal the legs should
lift much higher if need be 5) Anticipation
handles touch sensors (whiskers) to detect
obstacles 6) Pitch stabilization uses an
inclinometer to stabilize fore/aft pitch 7)
Prowling uses infrared sensors to start walking
when a human approaches 8) Steering uses the
difference in two IR sensors to follow
57 FSMs wired together !
47
Brooks Approach
  • Dont use logic-based description of percepts
  • Dont apply search operators or logical inference
    or planning operators
  • Goal formalized, but not generalized
  • Arrive at a next action, not operator sequence
  • Apply that operator sequence to world (detail)
  • Respond based on contingencies interaction

48
Levels for the Genghis Robot
  • Level1 standup
  • 2 modules per leg control alpha (advance) beta
    (balance) motor
  • Level2 simple walk
  • does not compensate for rough terrain
  • Level3 force balancing
  • Compensates for rough terrain
  • Level4 leg lifting
  • Level5 whiskers
  • Level6 pitch stabilization
  • Level7 steered prowling

49
Layers and modules
  • Each layer consists of a number of modules
  • Modules
  • Module is a hardware component
  • CPU memory specialized hardware program
  • Modules connect to other h/w components via
    physical wires
  • Connections are fixed by design not dynamic
  • Connections between modules are low bandwidth
  • Modules within a layer
  • Interconnect with data inputs outputs
  • Modules are asynchronous
  • No centralized clock
  • No central memory

50
Conflict resolution on a path from sensors to
actuators
Module
Example of suppression
51
A Behavior-based Architecture for Box Pushing
Global reward
Local Rewards on all levels
52
Connections between layers
  • Layer0 is lowest
  • Layer1 is next
  • Etc.
  • Layers are designed to have separate goals
  • Lower layers deal with more important goals
  • E.g., important survival
  • Connections across layers
  • Layer1 connects to Layer0 layer2 layer 1 etc.
  • Connections always from higher level to lower
    level
  • Lower layer cannot detect this connection
  • Connections are fixed by design (not dynamic)
  • Established by physical wires

53
Suppression and Inhibition in modules
  • These connections across layers are of two types
  • Suppression Input lines
  • When layer1 sends a message on this connection,
    layer1 input replaces layer0 output
  • Inhibition Output lines
  • When layer1 sends a message on this connection,
    layer1 causes the layer0 output to be stopped
    (for an interval)
  • No output replacement
  • Modules are callable sub-functions (or
    procedures)
  • Cant make a procedure call and invoke a module
  • Can send a message to a module if you have a wire
    from one module to another module
  • Connections are for message passing (within
    layers) and suppression/inhibition (across
    layers)

54
Explanation of the layers for a mobile robot
  • Avoid contact with objects (moving or static)
  • Wander aimlessly around without hitting things.
  • Explore the world by seeing places in the
    distance which look reachable and heading for
    them.
  • Build a map of the environment and plan routes
    from one place to another
  • Notice changes in the static environment
  • Reason about the world in terms of identifiable
    objects and perform tasks related to certain
    objects.
  • Formulate and execute plans which involves
    changing the world in some desirable way
  • Reason about the behavior of the objects in the
    world and modify plans accordingly

55
Emergent Behaviors in a Subsumption Architecture
  • If the sensory inputs satisfy a precondition
    specific to that module, then a certain behavior
    program, also specific to that module, is
    executed
  • One behavior module can subsume another behavior
  • Complex behaviors can emerge from the interaction
    of a relatively simple reactive machine with
    complex environment

56
Advantages of Subsumption Architecture
  • Robustness
  • Reactive and real-time response
  • Flexible multi-goal, easy to extend
  • The robot interacts with the real world, not
    symbols
  • No explicit representation of goals needed
  • No central control unit needed
  • No mapping from sensor values to physical
    quantities needed

57
Questions concerning limits of Subsumption
Architecture
  • our approach
  • How many layers can be built?
  • How complex can a behavior be?
  • Can higher level functions such as learning be
    achieved by this approach?

58
Subsumption Architecture is a novel idea about
mobile robots in history.
  • It brings some brand new ideas to all kind of
    robotics, not only mobiles.
  • It breaks the traditional solely sequencing mode
    to a parallel mode.
  • To some extent, it has actually affected the
    standard control architecture today.

. it affected also the AI..
59
New Artificial Intelligence
  • Reactive vs. Representational
  • Emergent approaches (implicit)
  • Braitenburg vehicles
  • swarming, flocking
  • Behavioral approaches (explicit)
  • Brooks, Steels
  • Physical Embodiment

60
Levels (layers) of Control
61
Communication among competencies
1. different sensors can be read by different
competencies
2. competencies such as observe can read outputs
from other competencies
observe
avoid obstacles
bump sensors
3. competencies can also write values into input
of other competencies
S
follow line
IR sensor
S
forage
motors
S
62
Augmented Finite State Machine
R
Sensor
Finite State Machine
R
R
63
Layers are composed of modules
  • An example of module is shown below

Inhibitor
1
3
Behavioral module
Inputs
Outputs
S
10
Suppression and Inhibition
Suppressor
64
More general behavior interactions suppression
and inhibition can be on both inputs and outputs
arbitrates among layers in an ad-hoc way
AFSK of a simple three-layered robot from Brooks
65
Other examples of Subsumption Architectures
  • Layer 0

robot
robot
collide
halt
motor
sonar
command
feelforce
runaway
force
66
Layer0Layer1
robot
robot
collide
halt
sonar
motor
S
command
15
feelforce
runaway
force
We can suppress runaway behavior
avoid
wander
heading
Layer 1
67
Another extension Layer 0
turn
68
Layer 0
Subsumption Architecture
runaway behavior
FSM / DFA
69
Layer 1 added
wander behavior
70
Il livello 2
Layer 2 added
navigate behavior
wander behavior
runaway behavior
71
Another Example of Subsumption Architecture
Mapping robot
72
Subsumption
Another Example of Subsumption Architecture
Levels of competence
  • A number of levels of competence are defined
  • an informal specification of a desired class of
    behaviors for a robot over all environments it
    will encounter
  • A higher level of competence implies a more
    specific desired class of behaviours

General purpose reasoning approach on the top
level
Control is layered, with higher level layers
subsuming the roles of lower level layers when
they wish to take control. The system can be
partitioned at any level, and the layers below
form a complete operational control system.
73
Example Competencies in more detail
7 reason about the behaviour of objects in the
world and modify plans accordingly
6 formulate and execute plans that involve
changing the state of the world in some desirable
way
5 reason about the world in terms of identifiable
objects and perform tasks related to certain
objects
4 notice (observe) changes in the static
environment
3 build a map of the environment and plan routes
from one place to another
2 explore the world by seeing places in the
distance that look reachable and heading for them
1 wander aimlessly around without hitting things
0 avoid contact with (stationary or moving)
obstacles
74
Step-by-step building of Layers of Control
  • The key idea of levels of competence is that
    layers of a control system can be built to
    correspond to each level
  • a control system can be built to achieve level
    zero competence (avoid obstacles)
  • it is programmed, debugged and then fixed in
    operation
  • another layer is then added (first-level control)
  • it is able to examine data from the level-0
    system
  • it is able to inject output, to suppress the
    level-0 output
  • level-0 is unaware of the suppression and
    continues to run

75
Structure of Layers
  • How is each level built?
  • does each have to be structured in horizontal
    manner?
  • This is true to a certain extent, but the key
    difference is that not all desired perceptions
    need to be processed by each competence
  • different decompositions can be used for
    different sensor-set task-set pairs
  • Layers can be built on a set of small processors
  • each one sends (small) messages to others
  • no message acknowledgement is required
  • there is no need for central control (or
    synchronisation)

76
subsumption details
  • Each layer has one function, conceptually
  • Lower layers tend to be more reactive
  • closed loop controls
  • inputs tightly coupled to outputs
  • Higher layers are more deliberative
  • do higher-level sensor fusion modeling
  • keep more state
  • planning further in the future
  • Layers can fake the inputs or outputs of other
    layers

77
subsumption advantages(according to Brooks)
  • Provides a way to incrementally build and test a
    complex mobile robot control system
  • Supports parallel computation in a
    straightforward, intuitive way
  • Avoids centralized control relies on
    self-centered and autonomous modules
  • Leads to more emergent behavior -- Complex (and
    useful) behavior may simply be the reflection of
    a complex environment
  • Compare with SPA - intelligence is entirely in
    the design of the planner (the programmer)

78
subsumption successes
  • Early efforts were a dramatic success, zipping
    around like R2D2 instead of pondering their plans
  • Pinnacle was Herbert, who found soda cans in an
    office

Criticism of Subsumption
  • Herbert didnt work very repeatably
  • No subsumption-based robot since Herbert -- or is
    there?
  • Is classical subsumption still in use?
  • Cog is based on subsumption?
  • Brooks publications, however, mainly describe
    imitation of human cognitive models and do not
    explicitly mention subsumption
  • But, these models also stress non-monolithic
    control subsumption might be there implicitly

79
Criticism of Subsumption
  • More complex behaviors?
  • This approach wont necessarily lead to system
    capable of more complex behaviors. A new
    controller is needed for each task.
  • 2. Is it an evolution?
  • The experimenter is deciding on what modules to
    add, and what environment and task the robot
    should be exposed to. - not the same as
    evolution.
  • But in terms of evolution, new behaviors and new
    mental structures are learnt in response to the
    environment, not added by an experimenter.
  • Similarly, in the development of an individual,
    new representational structures are developed in
    response to the environment, not added by an
    experimenter.

80
Criticism of Subsumption
  • 3. It would be more impressive if the robot
    learnt new behavior modules in response to the
    environment.
  • This possibility is discussed by Brooks (1991),
    but not yet successfully tackled.
  • 4. Emphasis in this approach on reacting to the
    environment.
  • And it is the case that apparently quite
    sophisticated behaviors can result from simple
    reaction to the environment.
  • But representations are needed for more complex
    tasks.
  • e.g. Find an empty can and bring it back to the
    starting point requires the formation of an
    internal representation corresponding to a map.
  • Need to provide an account of the development of
    representations.

81
Other Reactive Approaches
82
Other Reactive Approaches
  • Two other reactive approaches that are popular
  • Potential field methods
  • a potential field is a concept from physics
  • two examples are the gravitational field
  • you do not need to be told which way to fall
  • planets do not need to plan how to move around
    the sun
  • and electromagnetic fields
  • mobile phones, television, radio, etc.
  • e.g. obstacles exert hypothetical repulsive
    forces on the robot
  • Motor schema navigation
  • multiple, concurrent schema generate separate
    behaviors which are summed to produce output
  • schema are dynamically created/destroyed as needed

83
Embedding not intelligence, but capabilities,in
everyday objects is one crucial part of the
solution.
  • --Pattie Maes

84
Reactive vs. Algorithmic Control
  • Algorithmic Control robots program is
    fundamentally a series of steps or actions to be
    taken in a predetermined order.
  • Most effective when the robots world and its
    interactions with it are well-structured
  • Manipulator arms in factories typically use
    algorithmic control with great success
  • Loses its appeal when the robot must deal with
    unexpected situations
  • When it is extended to deal with error
    situations, the algorithmic method becomes a
    complicated tree of branching decisions that is
    hard to design and debug
  • Reactive Control robots program is organized
    around a collection of separate mini-programs,
    all running at once and able to take control of
    the robot as they see fit
  • For a very simple, minimal HandyBug program,
    there might be
  • a touch sensor process, which monitored the
    robots touch sensors and caused the robot to
    back up and turn when it hit something
  • a periodic turn process, that caused the robot to
    take a turn every now and then
  • and a wander process, that caused the robot
    simply to move
  • Reactive control excels in complex situations
    with many unpredictable interactions

85
Behaviour-based Robotics
  • Idea of building autonomous mobile robots.
  • New approach, where robots operate in the world,
    and use highly reactive architectures, with no
    reasoning systems, no manipulable
    representations, no symbols, and totally
    decentralized computation (Brooks, 1991)
  • I wish to build completely autonomous mobile
    agents that co-exist in the world with humans,
    and are seen by those humans as intelligent
    beings in their own right. I will call such
    agents Creatures... (Brooks, 1991)
  • Brooks, R. (1991) Intelligence without
    Representation Artificial Intelligence, 47,
    139-159.

86
  • See Elephants dont play chess, (1990 paper by
    Brooks)
  • Brooks, Rodney A. (1990) Elephants dont play
    chess. In Pattie Maes (Ed) Designing autonomous
    Agents, Cambridge, Mass MIT Press.
  • Because elephants dont play chess, no reason to
    assume they are not intelligent.
  • Emphasis on kind of behaviour exemplified by
    elephants, rather than on more abstract human
    behaviours (e.g. games, speech recognition,
    problem solving).

87
  • A Creature must cope appropriately and in a
    timely fashion with changes in its dynamic
    environment.
  • A Creature should be robust with respect to its
    environment minor changes in the properties of
    the world should not lead to total collapse of
    the Creatures behaviour rather one should only
    expect a gradual change in the capabilities of
    the Creature as the environment changes more and
    more.
  • A Creature should be able to maintain multiple
    goals and, depending on the circumstances it
    finds itself in, change which particular goals it
    is actively pursuing thus it can both adapt to
    surroundings and capitalize on fortuitous
    circumstances.
  • A Creature should do something in the world it
    should have some purpose in being.

88
  • Set of principles (Brooks, 1991)
  • The goal is to study complete integrated
    intelligent autonomous agents.
  • The agents should be embodied as mobile robots
    situated in unmodified worlds found round
    laboratory. (embodiment).
  • Robots should operate under different
    environmental conditions - e.g. in different
    lighting conditions, when sensors and actuators
    drift in calibration (situatedness).
  • Robots should operate on timescales commensurate
    with timescales used by humans (situatedness).

89
Hybrid Control Architectures
90
Robot Architecture
how much / how do we represent the world
internally ?
As much as possible.
SPA architecture
  • Sense -- plan -- act design of Shakey and the
    Stanford Cart
  • Some tasks do require a deliberative approach,
    i.e., reasoning about the world.
  • Current robots incorporate both reaction and
    reasoning. (sometimes termed hybrid systems)

91
Standard Approach - 1
world
world
perception
cognition
action
92
Standard Approach - 2
  • Agent design we have been considering
  • Sequential flow
  • Percepts are obtained from sensors in world
    (somehow)
  • Get a logic-based or formal description of
    percepts
  • E.g., wumpus world percepts
  • We apply search operators or logical inference or
    planning operators
  • General (replaceable) formal goal
  • Arrive at some operator or operator sequence
  • Apply that operator sequence to world (somehow)

93
Taxonomy of Control Architectures hybrid
  • A variety of different approaches have been tried
    for implementing the sense-plan-act control cycle
  • These approachescan be categorised as
  • model-based
  • reactive
  • hybrid

model based
reactive
behavior based
Three-Layer Architecture is an example of hybrid
system
94
Examples of Hybrid Approaches
  • The SSS three-layer architecture
  • the servo-subsumption-symbolic architecture
    combines Brooks architecture with a lower-level
    servo control level and a higher-level symbolic
    system Connell
  • Fuzzy logic and neural network controllers
  • fuzzy logic rule-base(s), neural network(s) and
    combinations of both take inputs from sensors and
    process the data to generate output to actuators

95
Learning Approaches
  • Traditional learning techniques
  • rather than attempt to predefine and predict a
    symbolic model of the real-world, the robot
    learns how to operate and how to behave by
  • supervised learning
  • desired output is known for each set of input
    settings (e.g. ANNs)
  • reinforcement learning
  • learning by trial and error through performance
    feedback
  • Evolutionary algorithms
  • using genetic algorithms to find good network
    weights
  • significant problems with evolving real solutions
    in reasonable time on current mobile robot
    hardware

96
Control Problems for an autonomous robot
  • Rapidly changing boundary conditions
  • Real-time response
  • Information collected over noisy channels

97
The Control Cycle
  • A fundamental methodology derived in the early
    days of robotics from engineering principles is
    the sense-think-act cycle
  • the principle is to continuously attempt to
    minimise the error between the actual state and
    the desired state
  • based on control theory

sense
98
Model Based
  • A symbolic internal world-model is maintained
  • the sub-tasks are decomposed into functional
    layers
  • similar to classical artificial intelligence
    approach

sense
modelling
planning
task execution
motor control
99
Problems with Models
  • An adequate, accurate and up-to-date model must
    be maintained at all times
  • this is very difficult in practice!
  • suppose, for example, the sensors detect an
    object that we have not got a symbol for (a novel
    object)
  • A model-based system is extremely brittle
  • if one of the functional layers fails (e.g.
    hardware problems, software bugs), then the whole
    system fails
  • Significant processing power is required
  • maintaining the model takes time, so slow
    responses!?
  • Despite much effort, little progress was made!

100
Reactive Controllers
  • In order to try to overcome the shortcomings of
    model-based robots, modern approaches have
    centred predominantly on simple reactive systems
    with minimal amounts of computation
  • model-free systems
  • More correctly, the models are simple and
    implicit
  • the systems do not use symbolic models but, for
    example, a rule-set which tells a robot how to
    react to a corner when following a wall may be
    considered to be a simple, implicit model
    fragment
  • it implicitly encodes assumptions about the
    environment

101
Behavior Based
  • The control system is broken down into horizontal
    modules, or behaviors, that run in parallel
  • each behavior has direct access to sensor
    readings and can control the robots motors
    directly

sense
102
Three-layer architectures (TLA)
103
Three-layer architectures (TLA)
  • Response to subsumption, simultaneously and
    independently developed by gt3 groups
  • TLA design seems to implicitly
  • Agree that different processing models are needed
    to react to events on different time scales
  • Agree with loose asynchronous interfaces
  • Disagree with the infinite regression of layers
  • Disagree with the subsumption mechanism itself --
    i.e. overriding of inputs/outputs
  • But is this the essence of subsumption?

104
the role of state
  • SPA
  • Uses extensive internal state
  • Plans slowly and infrequently
  • Gets into trouble when its internal state loses
    synchronicity with the world
  • Reactive
  • The World is its Own Best Model
  • No internal state
  • Tight sensor to actuator coupling
  • Runs headlong into the problem of extracting
    state information from the world using sensors
  • Hybrid/Three Layer
  • Cant we all just get along?

105
the three-layer architecture
  • Consists of (surprise!) 3 layers
  • Reactive layer (Controller)
  • Stateless, sensor-based
  • Short time scale actions
  • Glue Layer (Sequencer)
  • Has a memory of the past
  • Selects primitive behaviors for Controller
  • Planning Layer (Deliberator)
  • Plans for the future
  • Time-consuming operations (search, complex
    vision, etc.)

106
Behavior Advantages
  • It supports multiple goals and is more efficient
  • there is no functional hierarchy between layers
  • one layer does not call another layer
  • each layer can work on different goals in
    parallel
  • communication between layers is achieved via
    message passing which need not be synchronised
  • The system is easier to design, debug and extend
  • each module can be designed and tested
    individually
  • The system is robust
  • if one module fails, e.g. wander, then other
    layers, e.g. avoid obstacles, still function and
    behave correctly

107
Behavior Limitations
  • It is extremely difficult to implement plans
  • in pure form a behaviour-based robot has no
    memory (not even an internal state memory) and so
    is unable to follow an externally specified
    sequences of actions
  • It can be very hard to predict how a large number
    of multiple behaviours may interact
  • emergent behaviour is the term given to
    unexpected behaviour that comes about through
    these interactions
  • sometimes it is useful, sometimes it is not!
  • The robot can get trapped in a limit cycle
  • trapped in a dead-end, repeatedly turning left
    then right

108
The new approach
  • A robust layered control system based on task
    (activity) achieving
  • What is the layer?
  • A class of behavior
  • Requirements
  • Multiple goals
  • Multiple sensors
  • Robustness
  • Extensibility

109
Conclusions Implications
  • A kind intelligent action can be generated
    without
  • Specific logical representation
  • Logical reasoning or search
  • Intelligence without typical representation
  • Can planning, and other traditional AI goals be
    met?

110
Background
  • Agent an entity with domain knowledge, goals
    and actions.
  • Chess playing programs
  • Sojourner Mars Exploration Robot
  • Autonomous Agent uses sensors and effectors to
    independently achieve goals.
  • Mobile Robots and Vehicles

111
Box-Pushing Task
  • Consider the following task design a
    behavior-based robot to push boxes
  • What should the design look like?
  • What are the behaviors?
  • How should they be combined?

112
Behaviors for Box-Pushing Task
  • FINDER
  • Goal Locate an object that might be a box
  • Strategy Wander around till the sonars detect an
    object
  • Priority Lowest (default behavior)
  • PUSHER
  • Goal Push an object
  • Strategy Move forward while bumped and not stuck
  • Priority Override FINDER when active
  • UNWEDGER
  • Goal Recover from stalled situations where
    PUSHER failed
  • Strategy Turn and move forward to open space
  • Priority Highest (override all other behaviors)

113
Control Flow Among Behaviors
114
Recycling Task
  • Consider designing a behavior-based robot to
    recycle soda cans into trash bins
  • What sensors would you use?
  • What features would you design?
  • What kinds of behaviors would be necessary?
  • Dont peek at Connells paper yet!

115
Behaviors for Recycling
  • Find soda cans
  • Pick up a soda can
  • Navigate the environment
  • Avoid obstacles
  • Find trash cans
  • Head for home

116
HERBERT A Coke Can Collecting Robot
  • Herbert was designed by Jonathan Connell for his
    Ph.d. thesis at MIT
  • Herbert was one of the first demonstrations of
    behavior-based robots
  • This robot demonstrated that a distributed
    colony of behaviors could be combined to do a
    nontrivial robotics task
  • There is no central coordinator or master
    process underlying Herbert

117
Strategies for Combining Behaviors
  • Subsumption
  • One behavior subsumes another (overrides it)
  • Easy to implement
  • Combination may result in thrashing or jerky
    motions
  • Bitwise
  • Each behavior modifies action command bits
  • Vector sum (schema approach)
  • Each behavior proposes a certain motion vector
  • The motion vectors are combined through vector
    addition
  • The resultant vector is the direction of motion
    of the robot
  • Smooth combination of behaviors

118
Conflicting Behaviors
Find box
Avoid
Wall
119
Bitwise Combination of Behaviors
ACTION COMMAND 1 BYTE
1
1
0
0
1
0
0
1
Turn fast
Turn left
Turn right
Turn med.
Trans. fast
Trans. slow
Back
Forward
Each behavior modifies certain fields of the
action command logical BIT and logical BIT OR.
120
Vector Sum Combination of Behaviors
Potential field motion planning obstacles are
repulsive , goals are attractive
GOAL
AVOID
OBSTACLE
MOVE to GOAL
121
Limitations of Behavior-Based Robots
  • No easy way to incorporate global knowledge
    (symbolic maps, rules etc.)
  • Hardwired behaviors -- robot cannot adapt to new
    unforeseen situations
  • Lacks a planning/reasoning component -- cannot
    predict consequences of actions
  • Extensions
  • New behaviors can be learned using neural
    networks and reinforcement learning
  • Global knowledge and planning achieved using a
    higher level deliberative system on top of
    behavior-based system

122
A Layered Robot Navigation Architecture
Sensor Reports
Planning and Execution Layer (POSMDP)
Neural Net Features
Action Commands
Action Reports
Local Occupancy Grids
Behavior-based Layer
Motor Commands
Raw Sensor Values
123
Limitations of Reactive Systems
  • Action computed depends on current sensor values
    only
  • State history is not captured
  • Hard to write complex monolithic reactive systems
  • Solution
  • Decompose overall system into a number of
    behaviors
  • Use small amounts of state information as
    memory
  • Many robots have been successfully built using
    this approach

124
Hybrid Architecture
symbolic
Control values
sensors
actuators
125
Hybrid architecture for navigation
126
Future Work
  • Short Term
  • Galloping of Legged Robots
  • Humanoid Robots for Soccer
  • Long Term
  • Creation of highly skilled Robots
  • Search and Rescue Operations

127
Research Problems
128
Vision
  • Fully Colored Environment
  • Teams (Red Dark Blue)
  • Identify Teammates
  • Ball (Orange)
  • Goals (Light Blue)
  • Poles
  • 6 combinations of Yellow, Pink, and Green
  • Localization

129
Vision
  • YUV color model
  • Y represents luminance
  • U and V represents chromacity
  • Color classification algorithm
  • 20 images used for training
  • Convergence in less than one hour
  • High accuracy

130
Localization
  • Bayesian Probabilistic Localization
  • Bayes Theorem
  • P(SiO) P(Si) P(OSi) .
  • ?j P(Sj) P(OSj)
  • Movement Algorithm
  • P(SiM) ?j P(Sj) P(Sj ? SiM)

131
RoboCup Agenten
  • PS Anwendungen der agentenorientierten
    Programmierung
  • Vortrag von Martin Lötzsch und Matthias Jüngel
  • loetzsch_at_informatik.hu-berlin.de
  • juengel_at_informatik.hu-berlin.de

132
Ideas
  • Evolve state machines
  • Learn State Machines
  • Combinational (mv, fuzzy,arithmetic logic)
    functions are special cases of state machines
  • They can be learned using Constructive Induction
  • formal optimal construction methods based on
    logic synthesis programs which are very efficient
  • Neural Nets can be included in layers

133
Questions ?
134
SUMMARY continues
  • Robotics is challenging field for to reasons
  • First , it requires hardware(sensors and
    effectors) that actually work, a real challenge
    for mechanical engineering
  • Second, robots have to work in the physical
    world, which is more complex than most of the
    simulated software worlds that we have used for
    our examples in other chapters
  • But modern autonomous robots with sophisticated
    sensors and effectors provide a challenging
    testbed for determining what it takes to build an
    intelligent agent

135
Summary
  • FIRA
  • Since 1996
  • MiroSot, NaroSot, KheperaSot, RoboSot
  • RoboCup
  • Since 1997
  • Simulation league, Small size league,Middle size
    league, Sony legged robot league

There is a similarity to international boxing.
136
Summary
  • Summary of this lecture
  • background
  • the behaviour based approach
  • Brooks assumptions about mobile robot design
  • subsumption architecture
  • levels of competence
  • example competences
  • layers of control
  • subsumption
  • structure of layers
  • extensions finite state machines
  • Next lecture
  • Brooks subsumption architecture practice

137
  • 7. SUMMARY
  • The Physical World
  • Industry applications
  • Body with rigid links connected to each other by
    joints
  • Sensors like vision, force, tactile, sonar
  • The problem of moving a complex-shaped object(
    i.e., the robot and anything it is carrying)
    through a space with complex-shaped obstacles is
    a difficult one. The mathematical notation of
    configuration space provides a framework for
    analysis.
  • Cell decomposition and skeletonization methods
    can be used to navigate through the configuration
    space. Both reduce a high dimensional, continuous
    space to a discrete graph-search problem.
  • Some aspects of the world, such as the exact
    location of a bolt in the robots hand, will
    always be unknown. Fine-motion planning deals
    with this uncertainty by creating a sensor-based
    plan that will work regardless of exact initial
    conditions.
  • Uncertainty applies to sensors at the large scale
    as well. In the landmark model, a robot uses
    certain well-known landmarks in the environment
    to determine where it is, even in the face of
    uncertainty.
  • If a map of the environment is not available,
    then the robot will have to plan its navigation
    as it goes. Online algorithms do this. They do
    not always choose the shortest route, but we can
    analyze how far off they will be.

138
Conventional Approach vs. Behavior-Based Robots
(Brooks)
new AI
139
Reading
Autonomous Robot Teams in Dynamic and Uncertain
EnvironmentsProf. Manuela Veloso, Dr. Tucker
Balch, and Dr. Brett BrowningCarnegie Mellon
University
R. A. Brooks, A Robust Layered Control System
for a Mobile Robot, Cambrian Intelligence, The
MIT Press
J. O. Gray, D. G. Caldweel, Advanced Robotics
Intelligent Machines R. A. Brooks, Cambrian
Intelligence, The MIT Press
140
Subsumption Architecture
Book Computational Principles of Mobile Robotics
Papers Achieving Artificial Intelligence
through Building Robots
Famous paper, read the original!
Gregory Dudek and Michael Jenkin
Rodney Brooks
  • A Robust Layered Control Systemfor a Mobile
    Robot
  • Rodney A. Brooks
  • IEEE Journal of Robotics and AutomationVol.
    RA-2, No. 1, March 1986

141
links
  • Walking machines
  • http//www.fzi.de/ids/WMC/OtherWM.html
  • http//www.plustech.fi
  • http//www.ai.mit.edu/projects/leglab/robots/robot
    s.html
  • AI
  • http//www.ai.mit.edu/
  • http//www.ai.mit.edu/projects/cog/motor_video.htm

142
Sources
  • Rodney Brooks
  • Maja Mataric
  • Nilssons book
  • Jeremy Elson
  • Norvigs book, chapter 2. Good. Stimulus-Response
    Agents
  • English PH.D thesis, recent
  • Jon Garibaldi
  • Prof. Bruce Donald, Changxun Wu, Dartmouth
    College
  • Leo Ilkko
  • Prof. Manuela Veloso, Dr. Tucker Balch, and Dr.
    Brett BrowningCarnegie Mellon University
  • Rabih Neouchi , Donald C. Onyango and Stacy F.
    President
  • Axel Roth
  • Ramon Brena Pinero ITESM
  • Rhee, Taik-heon, Computer Science Department,
    KAIST
  • Brian R. Duffy, Gina Joue
  • Jon Garibaldi , De Montfort University
  • Lucy Moffatt, Univ of Sheffield
  • Yorick Wilks, Computer Science Department,
    University of Sheffield

Cecilia Laschi, Connell, Brooks Maja
Mataric Nilsson book Jeremy Elson Norvigs
book English PH.D thesis, recent Rhee, Taik-heon,
Computer Science Department, KAIST Axel
Roth Ramon Brena Pinero ITESM Rabih
NeouchiDonald C. Onyango and Stacy F. President
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