ROBOTICS

1

• ROBOTICS
• COE 584
• Robot Control Architectures

2
Review

• Control Architectures
• Languages for robot control
• Computability
• Organizing principles
• Architecture comparison criteria

3
Robot Control Architectures

• There are infinitely many ways to program a
  robot, but there are only few types of robot
  control
• Deliberative control (no longer in use)
• Reactive control
• Hybrid control
• Behavior-based control
• Numerous architectures are developed,
  specifically designed for a particular control
  problem
• However, they all fit into one of the categories
  above

4
Comparing Architectures

• Architectures can be classified by the way in
  which they treat
• Time-scale (looking ahead)
• Modularity
• Representation

5
Time-Scale and Looking Ahead

• How fast does the system react? Does it look into
  the future?
• Deliberative control
• Look into the future (plan) then execute ? long
  time scale
• Reactive control
• Do not look ahead, simply react ? short time
  scale
• Hybrid control
• Look ahead (deliberative layer) but also react
  quickly (reactive layer)
• Behavior-based
• Look ahead while acting

6
Modularity

• Refers to the way the control system is broken
  into components
• Deliberative control
• Sensing (perception), planning and acting
• Reactive control
• Multiple modules running in parallel
• Hybrid control
• Deliberative, reactive, middle layer
• Behavior-based
• Multiple modules running in parallel

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Representation

• Representation is the form in which the control
  system internally stores information
• Internal state
• Internal representations
• Internal models
• History
• What is represented and how it is represented has
  a major impact on robot control
• State refers to the "status" of the system
  itself, whereas "representation" refers to
  arbitrary information that the robot stores

8
An Example

• Consider a robot that moves in a maze what does
  the robot need to know to navigate and get out?
• Store the path taken to the end of the maze
• Straight 1m, left 90 degrees, straight 2m, right
  45 degrees
• Odometric path
• Store a sequence of moves it has made at
  particular landmark in the environment
• Left at first junction, right at the second, left
  at the third
• Landmark-based path

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Topological Map

• Store what to do at each landmark in the maze
• Landmark-based map
• The map can be stored (represented) in different
  forms
• Store all possible paths and use the shortest one
• Topological map describes the connections among
  the landmarks
• Metric map global map of the maze with exact
  lengths of corridors and distances between walls,
  free and blocked paths very general!
• The robot can use this map to find new paths
  through the maze
• Such a map is a world model, a representation of
  the environment

10
World Models

• Numerous aspects of the world can be represented
• self/ego stored proprioception, self-limits,
  goals, intentions, plans
• space metric or topological (maps, navigable
  spaces, structures)
• objects, people, other robots detectable things
  in the world
• actions outcomes of specific actions in the
  environment
• tasks what needs to be done, in what order, by
  when
• Ways of representation
• Abstractions of a robots state other
  information

11
Model Complexity

• Some models are very elaborate
• They take a long time to construct
• These are kept around for a long time throughout
  the lifetime of the robot
• E.g. a detailed metric map
• Other models are simple
• Can be quickly constructed
• In general they are transient and can be
  discarded after use
• E.g. information related to the immediate goals
  of the robot (avoiding an obstacle, opening of a
  door, etc.)

12
Models and Computation

• Using models require significant amount of
  computation
• Construction the more complex the model, the
  more computation is needed to construct the model
• Maintenance models need to be updated and kept
  up-to-date, or they become useless
• Use of representations complexity directly
  affects the type and amount of computation
  required for using the model
• Different architectures have different ways of
  handling representations

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An Example

• Consider a metric map
• Construction
• Requires exploring and measuring the environment
  and intense computation
• Maintenance
• Continuously update the map if doors are open or
  closed
• Using the map
• Finding a path to a goal involves planning find
  free/navigational spaces, search through those to
  find the shortest, or easiest path

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Simultaneous Mapping and Localization
15
Cooperative Mapping and Localization
16
Reactive Control

• Reactive control is based on tight (feedback)
  loops connecting a robot's sensors with its
  effectors
• Purely reactive systems do not use any internal
  representations of the environment, and do not
  look ahead
• They work on a short time-scale and react to the
  current sensory information
• Reactive systems use minimal, if any, state
  information

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Collections of Rules

• Reactive systems consist of collections of
  reactive rules that map specific situations to
  specific actions
• Analog to stimulus-response, reflexes
• Bypassing the brain allows reflexes to be very
  fast
• Rules are running concurrently and in parallel
• Situations
• Are extracted directly from sensory input
• Actions
• Are the responses of the system (behaviors)

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Mutually Exclusive Situations

• If the set of situations is mutually exclusive
• ? only one situation can be met at a given time
• ? only one action can be activated
• Often is difficult to split up the situations
  this way
• To have mutually exclusive situations the
  controller must encode rules for all possible
  sensory combinations, from all sensors
• This space grows exponentially with the number of
  sensors

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Complete Control Space

• The entire state space of the robot consists of
  all possible combinations of the internal and
  external states
• A complete mapping from these states to actions
  is needed such that the robot can respond to all
  possibilities
• This is would be a tedious job and would result
  in a very large look-up table that takes a long
  time to search
• Reactive systems use parallel concurrent reactive
  rules ? parallel architecture, multi-tasking

20
Incomplete Mappings

• In general, complete mappings are not used in
  hand-designed reactive systems
• The most important situations are trigger the
  appropriate reactions
• Default responses are used to cover all other
  cases
• E.g. a reactive safe-navigation controller
• If left whisker bent then turn right
• If right whisker bent then turn left
• If both whiskers bent then back up and turn left
• Otherwise, keep going

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Example Safe Navigation

• A robot with 12 sonar sensors, all around the
  robot
• Divide the sonar range into two zones
• Danger zone things too close
• Safe zone reasonable distance to objects
• if minimum sonars 1, 2, 3, 12 lt danger-zone and
  not-stopped
• then stop
• if minimum sonars 1, 2, 3, 12 lt danger-zone and
  stopped
• then move backward
• otherwise
• move forward
• This controller does not look at the side sonars

22
Example Safe Navigation

• For dynamic environments, add another layer
• if sonar 11 or 12 lt safe-zone and
• sonar 1 or 2 lt safe-zone
• then turn right
• if sonar 3 or 4 lt safe-zone
• then turn left
• The robot turns away from the obstacles before
  getting too close
• The combinations of the two controllers above ?
  collision-free wandering behavior
• Above we had mutually-exclusive conditions

23
Action Selection

• In most cases the rules are not triggered by
  unique mutually-exclusive conditions
• More than one rule can be triggered at the same
  time
• Two or more different commands are sent to the
  actuators!!
• Deciding which action to take is called action
  selection
• Arbitration decide among multiple actions or
  behaviors
• Fusion combine multiple actions to produce a
  single command

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Arbitration

• There are many different types of arbitration
• Arbitration can be done based on
• a fixed priority hierarchy
• rules have pre-assigned priorities
• a dynamic hierarchy
• rules priorities change at run-time
• learning
• rule priorities may be initialized and are
  learned at run-time, once or continuously

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Multi-Tasking

• Arbitration decides which one action to execute
• To respond to any rule that might become
  triggered all rules have to be monitored in
  parallel, and concurrently
• If no obstacle in front ? move forward
• If obstacle in front ? stop and turn away
• Wait for 30 seconds, then turn in a random
  direction
• Monitoring sensors in sequence may lead to
  missing important events, or failing to react in
  real time
• Reactive systems must support parallelism
• The underlying programming language must have
  multi-tasking abilities

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Designing Reactive Systems

• How to can we put together multiple (large
  number) of rules to produce effective, reliable
  and goal directed behavior?
• How do we organize a reactive controller in a
  principled way?
• The best known reactive architecture is the
  Subsumption Architecture (Rod Brooks, MIT, 1985)

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Vertical v. Horizontal Systems
Traditional (SPA) sense plan act
Subsumption
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Biological Inspiration

• The inspiration behind the Subsumption
  Architecture is the evolutionary process
• New competencies are introduced based on existing
  ones
• Complete creatures are not thrown out and new
  ones created from scratch
• Instead, solid, useful substrates are used to
  build up to more complex capabilities

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The Subsumption Architecture

• Principles of design
• systems are built from
• the bottom up
• components are task-achieving
• actions/behaviors (avoid-obstacles, find-doors,
  visit-rooms)
• components are organized in layers, from the
  bottom up
• lowest layers handle most basic tasks
• all rules can be executed in parallel, not in a
  sequence
• newly added components and layers exploit the
  existing ones

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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 (thus the name of the architecture)
• As layer 1 is added, layer 0 continues to
  function
• Continue designing layers, until the desired task
  is achieved

sensors
actuators
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Suppression and Inhibition

• Higher layers can disable the ones below
• Avoid-obstacles can stop the robot from moving
  around
• Layer 2 can either
• Inhibit the output of level 1, nothing gets
  through
• Suppress the input of level 1, signal is replaced
• The process is continued all the way to the top
  level

sensors
actuators
32
Subsumption Language and AFSMs

• The original Subsumption Architecture was
  implemented using the Subsumption Language
• It was based on finite state machines (FSMs)
  augmented with a very small amount of state
  (AFSMs)
• AFSMs were implemented in Lisp

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Subsumption Language and AFSMs

• Each behavior is represented as an augmented
  finite state machine (AFSMs)
• Stimulus (input) or response
• (output) can be inhibited or
• suppressed by other active behaviors
• An AFSM can be in one state at a time, can
  receive one or more inputs, and send one or more
  outputs
• AFSMs are connected communication wires, which
  pass input and output messages between them only
  the last message is kept
• AFSMs run asynchronously

collide
sonar
halt
34
Networks of AFSMs

• Layers represent task achieving behaviors
• Wandering, avoidance, goal seeking
• Layers work concurrently and asynchronously
• A Subsumption Architecture controller,
• using the AFSM-based programming
• language, is a network of AFSMs divided into
  layers
• Convenient for incremental system design

35
Wandering in Subsumption

• Brooks 87

The lowest level is our familar avoid objects
layer of control which includes avoidance and
halting behaviors. The next layer of control lets
the robot explore large areas. The third
level gives some extra heuristics for backing out
of tight situations.
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Layering in AFSM Networks

• Layers modularize the reactive system
• Bad design
• putting a lot of behaviors within a single layer
• putting a large number of connections between the
  layers, so that they are strongly coupled
• Strong coupling implies dependence between
  modules, which violates the modularity of the
  system
• If modules are interdependent, they are not as
  robust to failure
• In Subsumption, if higher layers fail, the lower
  ones remain unaffected

37
Module Independence

• Subsumption has one-way independence between
  layers
• With upward independence, a higher layer can
  always use a lower one by using suppression and
  inhibition
• Two-way independence is not practical
• No communication between layers is possible
• Do we always have to use these wires to
  communicate between parts of the system?

38
Using the World

• How can you sequence activities in Subsumption?
• Coupling between layers need not be through the
  system itself (i.e., not through explicit
  communication wires)
• It could be through the world. How?

39
A Robust Layered Control System for a Mobile
Robot, by Rodney A. Brooks.

• Layer Zero
• Takes a vector of sonar reading providing a robot
  centered map of surrounding obstacles.
• Collide monitor sonar map to determine whether
  there is an object ahead. If in the course of
  moving it comes to collide with something then it
  halts the robot.
• Feelforce generates a resultant (multiple
  direction) repulsive force. If something
  approaches then it forward the force to runaway
  in the right direction.
• Runaway it sends a command to motor if force is
  significant to avoid the force direction.
• Motor controlled by using two dofs that are the
  speed and the steering angle.

40

• Layer One
• Objective Wander around aimelessly (depends on
  level Zero)
• Wander generates new heading for the robot every
  10 seconds.
• Avoid takes force vector from feedforce and
  combine it with heading to produce modified
  heading. Avoid subsumes the runaway module as
  it may suppress its output in the case of a new
  heading to account for.

• Layer Zero
• Takes a vector of sonar reading providing a robot
  centered map of surrounding obstacles.
• Collide monitor sonar map to determine whether
  there is an object ahead. If in the course of
  moving it comes to collide with something then it
  halts the robot.
• Feelforce generates a resultant (multiple
  direction) repulsive force. If something
  approaches then it forward the force to runaway
  in the right direction.
• Runaway it sends a command to motor if force is
  significant to avoid the force direction.
• Motor controlled by using two dofs that are the
  speed and the steering angle.

41

• Layer Two
• Objective Exploratory mode using visual
  obersver to select interesting places to visit.
  Uses position servoing the robot to a desired
  position over a travel path including some local
  obstacles.
• Grabber reads a global Goal. Ensures control by
  sending a halt to motor, this is done by
  inhibiting (See the many inhibiting lines of
  control) the lower level, to give time to plan a
  detailed motion. A goal is sent to pathplan
  module.
• Inhibiting connection to level 1 so that no
  action can be initiated. The robot cannot
  approaching objects for 2 seconds.
• Monitor track each motion by monitoring the
  status of motor and when motor is inactive it
  queries the robot to read the shaft encoder to
  find how far it traveled, whether it terminated,
  as supposed to, or it is in an early halt due to
  obstacles.
• Integrate accumulates reports of motion from the
  monitor and sends its most recent result out of
  its integral line. It is restarted by a reset
  signal.
• Pathplan takes a goal (angle to turn, a distance
  to travel, and a final orientation) and attempt
  to reach goal. It sends heading to Avoid, which
  may perturb them to avoid local obstacles, and
  monitors the integration of actual motion. It may
  suppress the random wanderings at input of Avoid
  as long as higher level planner remains active.
  When the robot is at goal it outputs the goal to
  Straighten.
• Straighten modifying the final orientation of
  the robot. Perform fine-grain control of robot
  orientation without being exposed to feelforce.
  For this it directly sends its control to motor
  and monitor Integral for completness.It also
  inhibits the Collide because there is no chance
  of collision from forward motion.

Level 2 control lower layers using four lines (I
and S). A planned stereo system depth data
produce of corridor space. Extra control is
needed to stop the robot and take additional
views when the robot wander outside some limits
42
We wire finites state machines together into
layers of control. Each layer is built on top of
existing layers. Lower level layers never rely on
the existence of higher level layers
Intelligence without representation Rodney A.
Brooks
The last level is meant to add an exploratory
mode of behavior to the robot, using visual
observations to select interesting places to
visit. A vision module finds corridors of free
space. Additional modules provide a means of
position servoing the robot to along the corridor
despite the presence of local obstacles on its
path (as detected with the sonar sensing system).
The lower level two layers still play an active
role during normal operation of the second layer.
(In practice we have so far only reused the sonar
data for the corridor finder, rather than use
stereo vision.)
The next layer of control, when combined with the
lowest, imbues the robot with the ability to
wander around without hitting obstacles. This
controller level relies in a large degree on the
zeroth level's a version to hitting obstacles. In
addition it uses a simple heuristic to plan ahead
a little in order to avoid potential collisions
which would need to be handled by the zeroth level
The lowest level layer the robot does not come
into contact with other objects. If something
approaches the robot it will move away. If in the
course of moving itself it is about to collide
with an object it will halt. These two tactics
are sufficient for the robot to flee from moving
obstacles, perhaps requiring many motions,
without colliding with stationary obstacles. The
combination of the tactics allows the robot to
operate with very coarsely calibrated sonars and
a wide range of repulsive force functions.
43
Collecting Soda Cans

• Herbert collected empty soda cans and took them
  home
• Herberts capabilities
• Move around without running into obstacles
• Detect soda cans using a camera and a laser
• An arm that could extend, sense if there is a
  can in the gripper, close the gripper, tuck the
  arm in

44
Herbert

• Look for soda cans, when seeing one approach it
• When close, extend the arm toward the soda can
• If the gripper sensors detect something close the
  gripper
• If can is heavy, put it down, otherwise pick it
  up
• If gripper was closed tuck the arm in and head
  home
• The robot did not keep internal state about what
  it had just done and what it should do next it
  just sensed!

45
Tom and Jerry
Embodied Intelligence Tom and Jerry Gleb
Chuvpilo, Jessica Howe, MIT, 2002

• Tom and Jerry, as they are usually called, are a
  robotic cat and mouse pair.
• Both are implemented as robotic vehicles that
  are able to move around within their
  environments, as well as interact with each other
  and modify behavior based on their current
  surroundings.
• The mouse acts as a passive agent, simply moving
  and wandering through the environment with no
  sensory feedback, to give the cat something to
  chase.
• The cat is the active agent who tracks and
  chases this mouse around the environment, with a
  layering of multiple behaviors that are able to
  take effect at appropriate times.

46

• The Cat (Tom) has
• Two bump sensors, activated by front left and
  front right whiskers which give a Boolean
  pressed / not pressed value, while the light
  sensors return integer values between 0 and 255,
• Three light sensors mounted at the front of the
  vehicle, facing left, right, and forwards.

• Tom
• The mouse has got a light bulb on top of its
  head, and the cat has got light sensors to track
  down the mouse.
• The cats behavior is to wander around in the
  darkness while exploring as much space as
  possible, and go towards the light if there is
  one. If the cat finds the mouse it begins to play
  with it, by sitting still in the same place for a
  while, pouncing on it and batting it a bit, then
  letting the mouse go away. The mouse cant see
  the cat, so it just wanders around.

47

• The Mouse (Jerry)
• Is strictly passive and has no feedback in the
  form of sensors.
• Use light as the form in which the cat is able
  to track and chase the mouse.
• The mouse has a halogen bulb mounted on top of it
  which emanates light in all directions
• During the experiment the lights is turned off
  in the room.
• The light is used rather than infrared for mouse
  tracking because it is easy to debug light
  tracking mechanisms because the light is either
  shining or not shining.
• Using IR, on the other hand, is more difficult
  to debug simply because we cannot see with the
  naked eye what the robots see.

Jerry the mouse
Mouse is much smaller than Tom. The mouse also
has three wheels, two active and one passive.
However, a gearbox would be too bulky for the
robot of that size, so we decided to do without
it and use smaller wheels instead. The mouse has
no sensors, and its behavior is completely
deterministic. There is no randomness in the time
in which it goes forward or turns, as opposed to
the cat. On top of the mouse there is a source of
bright light (a hallogen lamp) so that the cat
would be able to see the mouse from far away. It
is worth noting that we had to decouple the
system and add another source of power.
48

• CAT Behaviors
• Four behaviors, each act in a layered fashion one
  upon another, so that the appropriate action is
  invoked at the appropriate time, subsuming the
  behavior of lower levels
• Layer 0 The basic action is to wander around
  the environment searching for the mouse, i.e.
  waiting for the light from the mouse to be seen
  so that a higher subsumption level may be called
  in.
• Layer 1 Follow light is activated by the three
  light sensors which allow the cat to turn towards
  the mouse and move towards it once the two side
  light sensors read at roughly the same levels.
• Layer 2 obstacle avoidance, which is activated
  by the bump sensors. When an obstacle is hit the
  cat will back up and turn away from it, after
  which the lower levels of wander or
  light-following will resume.
• Layer 3 the cat is playing with the mouse
  (complex) which is invoked when the cat is within
  a certain threshold range of the mouse and
  involves waiting, stalking, pouncing and freeing
  the mouse.

49

• Wander (CAT)
• Wandering aims at exploring the environment (in
  lack of stimulus that triggers other actions) in
  search of the mouse
• Repeatedly move forward for a random amount of
  time and then turn for a random amount of time,
  pointing it in a new direction.
• The random length of time is spread between a
  defined minimum and maximum amount of time.
• If in the process of wandering some stimulus is
  encountered the resulting behavior will take
  higher precedence over the wander action.
• When the action is complete wandering will
  resume.

50

• Light Following (CAT)
• When the cat is not pointing directly at the
  light the left and right light sensors will read
  different values. When these values are more than
  some defined delta apart the motors will spin to
  turn the cat in place so that the light is being
  faced head on.
• If both side sensors read roughly the same
  values and the forward sensor reads above some
  defined threshold (when it actually sees the
  mouse rather than ambient light levels) it will
  move forward, correcting its direction if need
  be.

51

• Obstacle Avoidance (bump level of CAT)
• The obstacle avoidance is activated by the
  whiskers of the cat, or the left and right bump
  sensors.
• When an obstacle is hit by either of these bump
  sensors the cat will slightly back up and turn
  away from the object that was hit.
• When an object is hit either by the side of the
  cat (as in when it is turning) or in a poor
  location along the front which does not allow the
  bump sensors to be compressed. In a case like
  this the wheels of the cat will keep spinning
  until either its body shifts enough to activate
  one of the bump sensors or to free the body from
  the obstacle, or when an outside influence such
  as the mouse approaching causes another action to
  take over (like turn towards the mouse.)
• There is also a tendency to turn away from
  objects based on the input from the light
  sensors. The tendency of the cat to turn towards
  the light also causes the cat to turn away from
  dark objects before they are reached.
• For example, if the cat is approaching a dark
  object at an angle, one side light sensor will
  begin to read a light level lower than the other
  side. This will cause the turn towards the
  light action to take over and the cat will in
  effect turn away from the object before the
  object is reached.
• This works best when the lights are on, and the
  behavior would be very visible if we ever changed
  mouse tracking based on light. When run with the
  lights off this behavior is not always apparent.

52

• Play (CAT)
• Play is a multi-stage behavior in which the cat
  stalks, pounces upon, and then lets the mouse
  escape. When the cat gets within a certain
  distance of the mouse the front light sensor hits
  a threshold level and the cat begins stalking the
  mouse. In this stage the cat sits still for a
  fixed length of time and just watches the mouse,
  rotating if necessary, but not moving towards it.
• If the mouse has stayed within the threshold
  distance of the mouse during this entire stalking
  time it then pounces upon the mouse moving
  towards the mouse at full speed until it hits it.
  Once it has hit the mouse it briefly backs up and
  then moves forward to hit it again. This is
  repeated until the mouse has been hit 3 times.
  The cat then sits still for a fixed amount of
  time, ignoring all sensory input, to allow the
  mouse time to escape. Once this fixed amount of
  time runs out playing is completed and standard
  subsumption style behavior is resumed.
• Play has the highest priority within the
  subsumption architecture, so while playing all
  other actions that would be performed by lower
  subsumption levels are ignored.
• One problem with the play mode is that there is
  no distinction between hitting the mouse and
  hitting a wall or obstacle while pouncing.
• While the cat is pouncing on the mouse it just
  moves at full speed towards the light until it
  hits something, which may be an obstacle rather
  than the mouse.
• As it is the play mode subsumes all other
  subsumption level behaviors so there is no way to
  both use obstacle avoidance and mouse chasing as
  written in those subsumption levels without
  writing that code directly into the play
  functionality. Perhaps one modification that
  would be made if we were to redesign the
  subsumption architecture would be to allow lower
  subsumption levels to be accessed while playing.

53

• Basic Architecture Design
• The main debate was between using subsumption
  architecture and finite state automata, as in the
  ants problem set. The trouble is to have complex
  FSA code.
• The FSA model would be much more complicated to
  implement. For this the subsumption architecture
  is selected by simply having a global variable
  for each output value passed by a block within
  the diagram to another, possibly subsuming
  another signal. If each of the functions
  representing diagram blocks is called, the
  functionality of each can depend on the current
  values of each of these global signals. In other
  words the previous signals are maintained during
  each time loop when each subsumption block
  calculates its desired output based on these
  input signals. It seemed to be a reasonable
  approach.
• Another advantage of subsumption is that we could
  start easy and build up to a more complex design
  as time allowed.

54
More on Herbert

• There is no internal wire between the layers that
  achieve can finding, grabbing, arm tucking, and
  going home
• However, the events are all executed in proper
  sequence. Why?
• Because the relevant parts of the control system
  interact and activate each other through sensing
  the world

55
World as the Best Model

• This is a key principle of reactive systems
  Subsumption Architecture
• Use the world as its own best model!
• If the world can provide the information
  directly (through sensing), it is best to get it
  that way, than to store it internally in a
  representation (which may be large, slow,
  expensive, and outdated)

56
Subsumption System Design

• What makes a Subsumption Layer, what should go
  where?
• There is no strict recipe, but some solutions are
  better than others, and most are derived
  empirically
• How exactly layers are split up depends on the
  specifics of the robot, the environment, and the
  task

57
Designing in Subsumption

• Qualitatively specify the overall behavior needed
  for the task
• Decompose that into specific and independent
  behaviors (layers)
• Determine behavior granularity
• Ground low-level behaviors in the robots sensors
  and effectors
• Incrementally build, test, and add

58
Genghis (MIT)

• Walk over rough terrain and follow a human
  (Brooks 89)
• Standup
• Control legs swing position and lift
• Simple walk
• Force balancing
• Force sensors provide information about the
  ground profile
• Leg lifting step over obstacles
• Obstacle avoidance (whiskers)
• Pitch stabilization
• Prowling
• Steered prowling

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The Nerd Herd (MIT)

• Foraging example (Mataric 93)
• R1 robots (IS robotics)
• Behaviors involved
• Wandering
• Avoiding
• Pickup
• Homing

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Tom and Jerry (MIT)
Tom

• fsdf

Jerry
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Pros and Cons

• Some critics consider the lack of detail about
  designing layers to be a weakness of the approach
• Others feel it is a strength, allowing for
  innovation and creativity
• Subsumption has been used on a vast variety of
  effective implemented robotic systems
• It was the first architecture to demonstrate many
  working robots

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Benefits of Subsumption

• Systems are designed incrementally
• Avoid design problems due to the complexity of
  the task
• Helps the design and debugging process
• Robustness
• If higher levels fail, the lower ones continue
  unaffected
• Modularity
• Each competency is included into a separate
  layer, thus making the system manageable to
  design and maintain
• Rules and layers can be reused on different
  robots and for different tasks

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Behavior-Based Control

• Reactive systems
• too inflexible, use no representation, no
  adaptation or learning
• Deliberative systems
• Too slow and cumbersome
• Hybrid systems
• Complex interactions among the hybrid components
• Behavior-based control involves the use of
  behaviors as modules for control

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What Is a Behavior?

• Behavior-achieving modules
• 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.)

65
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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Internal vs. Observable Behavior

• Observable behaviors do not always have a
  matching internal behavior
• Why not?
• Emergent behavior interesting behavior can be
  produced from the interaction of multiple
  internal behaviors
• Start by listing the desired observable behaviors
  
• Program those behaviors with internal behaviors
• Behavior-based controllers are networks of
  internal behaviors which interact in order to
  produce the desired, external, observable behavior

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An Example

• A robot that water plants around a building when
  they get dry
• Behaviors avoid-collision, find-plant,
  check-if-dry, water, refill-reservoir,
  recharge-batteries
• Complex behaviors may consist of internal
  behaviors themselves
• Find-plant wander-around, detect-green,
  approach-green, etc.
• Multiple behaviors may share the same underlying
  component behavior
• Refill-reservoir may also use wander-around

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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

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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