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Artificial Intelligence versus classical RoboticsAll robot
control architectures are build on some ideas of Artificial
IntelligenceThey form also, what the AI considered now, in contrast
to classical AIAL is the best exampleRobot control
architectures
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Is AI Engineering or Science?Construction ==> Engineeringall
scientific problems solvedrepresentative: FeigenbaumSciencemore
scientific principles to be discoverer representative: McCarthyIs
Robotics Engineering or Science?
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What is a robot? More definitions.An intelligent robot is a
machine able to extract information from its environment and use
knowledge about its world to move safely in a meaningful and
purposeful manner. A robot is a system which exists in the physical
world and autonomously senses its environment and acts in it.
Robotics is the intelligent connection of perception to action (M.
Brady)
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Alternative terms we will use: UAV: unmanned aerial vehicle UGV:
unmanned ground vehicle UUV: unmanned undersea vehicle What makes a
robot?sensors effectors/actuators locomotion system on-board
computer system controllers for all of the above (smart methods
everywhere)How these definitions relate to AI?Compare to classical
robot definitions.
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Sensing:What can be sensed?depends on the sensors on the
robotthe robot exists in its sensor space (i.e., all possible
values of its sensory readings, also called perceptual
space)robotic sensors are very different from biological sensors; a
designer needs to put his mind into the robot's sensor space a
roboticist has to try to imagine the world in the robots sensor
space
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What needs to be sensed?depends on the robot's task
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State: a sufficient description of the systemobservable: the
robot knows its state at all times
hidden/inaccessible/unobservable: the robot does not know its
statepartially-observable: the robot knows some part of its
statediscrete (e.g., up, down, blue, red) or continuous (e.g.,
3.765 mph)State space: all the states a system can be inExternal
state: state of the worldnight/day, raining/sunny, at home,
etc.sensed using the robot's sensors
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Internal state: state of the robothappy/sad, stalled/moving,
battery level, velocity, etc.can be sensed (e.g., velocity)can be
stored/remembered (e.g., happy/sad)The robot's state is a
combination of its external and internal state.How intelligent the
robot appears will strongly depend on how much and quickly it can
sense its environment and itself. We will talk more about sensors
in next lectures. Internal state can be used to remember
information about the world (e.g., remember paths to the goal,
remember maps, remember friends versus enemies, etc.) This is
called a representation or an internal model.
Representations/models have a lot to do with how complex a
controller is!
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Acting:A robot acts through the use of its actuators, also
called effectorsRobotic actuators are very different from
biological ones, both are used for: locomotion (moving around,
going places) manipulation (handling objects)This divides robotics
into three areas: mobile robotics manipulator robotics
communication robotics (theatre, toys)
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Acting:Action versus Behavior : Behavior is what an external
observer sees a robot doing. Robots are programmed to display
desired behavior. Behavior is a result of a sequence of robot
actions. Observing behavior may not tell us much about the internal
control of a robot. Control can be a black box. Mobile robots can
move around, using wheels, tracks, or legs, and usually move in
2-dimensions.However, swimming and flying robots are also mobile
robots, and they move in 3-dimensions (and are therefore even
harder to control)Manipulators are various robot arms; they can
move in 1 or more dimensions.the number of dimensions are called
the robot's degrees of freedom (DOF). we will learn much more about
actuators/effectors later.
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Autonomy:
What is autonomy? the ability to make one's own decisions and
act on themfor robots, the ability to sense the situation and act
on it appropriately Autonomy can be complete, as in autonomous
robots, or partial, as in tele-operated robots. examples of
autonomous robots: R2D2examples of tele-operated robots: NASA's
robots before PathfinderExo-skeletons are not robots, according to
our definition. (E.g., Ripley's exo-skeleton in the movie
Alien.)
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Fundamentals of Robot Control ArchitecturesDistinguish the
classical control used in robots and the Robot Control
Architectures that have more to do with AI
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Control:Robot control refers to the way in which the sensing and
action of a robot are coordinated. The many different ways in which
robots can be controlled all fall along a well-defined spectrum of
control. Control Approaches: Reactive Control : Dont think,
(re)act. Behavior-Based Control : Think the way you act.
Deliberative Control : Think hard, act later. Hybrid Control :
Think and act independently, in parallel.
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Control Trade-offs:Thinking is slow. Reaction must be fast.
Thinking enables looking ahead (planning) to avoid bad solutions.
Thinking too long can be dangerous (e.g., falling off a cliff,
being run over). To think, the robot needs (a lot of) accurate
information => world models.
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Food for Thought:Many robots you build in this class will use
reactive control. What more can you build on top of it? Your dream
robot?!Are exo-skeletons (e.g., Ripleys in the movie Alien) robots?
Is HAL (in the movie 2001) a robot? Some intelligent Web agents are
called "softbots". Are they robots?
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Please review:1. The concept of a Finite State Machine (a
sequential system)2. The design of a reactive system may include
using design automation tools (FPGA, EPLD) that you learn from
other classes.3. Review the stages of designing FSMs4. Recall
examples of FSMs5. Reactive machine may include counters, shifters,
adders, sequence generators, sequence recognizers or other that we
covered in ECE 271.
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Reactive Systems: Dont think, react! Reactive control is a
technique for tightly coupling perception (sensing) and action, to
produce timely robotic response in dynamic and unstructured worlds.
Think of it as "stimulus-response". A powerful method: many animals
are largely reactive.Limitations: Minimal (if any) state. No
memory. No learning. No internal models / representations of the
world.Reactive Robot Systems
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Reactive versus Deliberative SystemsReactive Systems Collections
of sense-act (stimulus-response) rulesrules implemented as assembly
code, C++ code, EPLD combinational logic, FPGA state machine, state
machine with stacks (memory), etc Inherently concurrent (parallel)
Very fast and reactive Unable to plan ahead
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Reactive versus Deliberative SystemsDeliberative Systems Based
on the sense->plan->act model Inherently sequential Planning
requires search, which is slow Search requires a world model World
models become outdated Search and planning takes too long
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Hybrid SystemsCombine the two extremes reactive system on the
bottom deliberative system on the top connected by some
intermediate layer Often called 3-layer systems Layers must operate
concurrently Different representations and time-scales between the
layers The best or the worst of both worlds???
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Behavior-Based SystemsAn alternative to hybrid systems Have the
same capabilities the ability to act reactively the ability to act
deliberativelyThere is no intermediate layer A unified, consistent
representation is used in the whole system => concurrent
behaviors That resolves issues of time-scale
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Feedback ControlFeedback: continuous monitoring of the sensors
and reacting to their changes. Feedback control = self-regulation
Two kinds of feedback: Positive NegativeThe basis of control theory
- and + Feedback Negative feedback acts to regulate the
state/output of the system e.g., if too high, turn down, if too
low, turn up thermostats, toilets, bodies, robots...Positive
feedback acts to amplify the state/output of the system e.g., the
more there is, the more is added lynch mobs, stock market, ant
trails...
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Feedback and CyberneticsUses of Feedback Invention of feedback
as the first simple robotics (does it work with our definition)?
The first example came from ancient Greek water systems (toilets)
Forgotten and re-invented in the Renaissance for ovens/furnaces
Really made a splash in Watt's steam engineCybernetics Pioneered by
Norbert Wiener (1940s) (From Greek "steersman" of steam engine)
Marriage of control theory (feedback control), information science
and biology Seeks principles common to animals and machines,
especially for control and communication Coupling an organism and
its environment (situatedness)
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W. Grey Walters Tortoise
Machina Speculatrix 1 photocell & 1 bump sensor, 1 motor
Behaviors: seek light head to weak light back from bright light
turn and push recharge batteryReactive control
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Turtle World (homework 2)Turtle Principles Parsimony: simple is
better (e.g., clever recharging strategy) Exploration/speculation:
keeps moving (except when charging) Attraction (positive tropism):
motivation to approach light Aversion (negative tropism):
motivation to avoid obstacles, slopes Discernment: ability to
distinguish and make choices, i.e., to adapt
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Turtle World (homework 2)Braitenberg Vehicles Valentino
Braitenberg (early 1980s) Extended Walters model in a series of
thought experiments Also based on analog circuits Direct
connections (excitatory or inhibitory) between light sensors and
motors Complex behaviors from very simple mechanisms By varying the
connections and their strengths, numerous behaviors result, e.g.:
"fear/cowardice" - flees light "aggression" - charges into light
"love" - following/hugging many others, up to memory and
learning!Reactive control Later implemented on real robots
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Artificial IntelligenceEarly Artificial Intelligence "Born" in
1955 at Dartmouth (thus both traditions are old!) "Intelligent
machine" would use internal models to search for solutions and then
try them out (M. Minsky) => deliberative model! Planning became
the tradition Explicit symbolic representations Hierarchical system
organization Sequential executionArtificial Intelligence (AI) Early
AI had a strong impact on early robotics Focused on knowledge,
internal models, and reasoning/planning Eventually (1980s) robotics
developed improved and innovative approaches => behavior-based
and hybrid control AI itself has also evolved... But before that,
early robots used deliberative control
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Early RobotsEarly Robots: SHAKEY At Stanford Research Institute
(late 1960s) Vision and contact sensors STRIPS planner Visual
navigation in a special world DeliberativeEarly Robots: HILARE LAAS
in Toulouse, France (late 1970s) Video, ultrasound, laser
range-finder Still in use! Multi-level spatial representations
Deliberative -> Hybrid Control
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Early Robots: CART/Rover Hans Moravec Stanford Cart (1977)
followed by CMU rover (1983) Sonar and vision Deliberative
control
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Robotics TodayAssembly and manufacturing (most numbers of
robots, least autonomous) Materials handling Gophers (hospitals,
security guards) Hazardous environments (Chernobyl) Remote
environments (Pathfinder) Surgery (brain, hips) Tele-presence and
virtual reality EntertainmentBoth approaches represented
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Why is Robotics hard?
Sensors are limited and crude Effectors are limited and crude
State (internal and external, but mostly external) is
partially-observable Environment is dynamic (changing over time)
Environment is full of potentially-useful information
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Key Issues of Robotics vs. AIGrounding in reality: not just
planning in an abstract world Situatedness (ecological dynamics):
tight connection with the environment Embodiment: having a body
Emergent behavior: interaction with the environment Scalability:
increasing task and environment complexity
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Food for thought. And Exam?...Argumentation:Try to argue that
robotics is an engineering and not scienceTry to argue on the
oppositeWrite an Eliza-like program with two robots arguing on this
topicSensing: Based on your knowledge from other classes, try to
invent a new sensor that has so far not been used much in robotics,
such as smell sensor, polarized light sensor or radiation sensor.
Some sensors may need a lot of processing. What computer software
and algorithms may be useful. Think for instance of having an array
of directed microphones.
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Food for thought. And Exam?...State: Give examples of various
types of states for your Turtle robot from homework 2. Using the
concept of finite state machines and verification of them, how can
you verify the correctness of actions of your robot, for instance
that it reaches the goal or does not bump to the obstacle. What can
be proven ?How to design a program that will analyze the
reachability of your robot in certain space?
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Food for thought. And Exam?...Control: Using example of your
Turtle, show examples of positive and negative feedback. Do you
have to redesign your control to be able to demonstrate both?
Control Architectures: Using your Turtle, give examples what
behaviors are reactive and what are deliberative. Perhaps most of
your Turtle behavior is reactive. How can you add planning on top
of reactive behaviors? What kind of plans will be the robot able to
execute.If a plan fails, what is the simple solution, using the
concepts that you learned so far?
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Food for thought. And Exam?...LearningAs you remember, any kind
of behavior that transforms the stored knowledge to a new form in
result of which the new behavior is more efficient, can be
categorized as learning, for instance, modifying the table of a
reactive state machine.Add one more layer to your Turtle, the level
of learning. How will you evaluate the quality of learning? Can GA
be a learning mechanism? How learning can be introduced in the
framework of tree search?Applications: Think about all possible
practical applications for your Turtle. What should be added to it
that it will remove mines from a former battlefield? That it will
be finding weeds and destroying them? Give characterization of
every task in terms of basic control architectures from the
class