Download Lecture 18 Robots Introduction

Robots
Introduction
Based on the lecture by Dr. Hadi Moradi
University of Southern California
Outline
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Control Approaches
Feedback Control
Cybernetics
Braitenberg Vehicles
Artificial Intelligence
Early robots
Robotics Today
Why is Robotics hard
Control
• Sensing => Action
• Reactive
– Don’t think, act: Animals
• Deliberative
– Think hard, act later: Chess
• Hybrid
– Think and act in parallel: car races
• Behavior-based
– Think the way you act: human
Reactive Systems
• Collection of sense-act rules
– Stimulus-response
• Advantages:
–?
• Disadvantages
–?
Reactive Systems
• Collection of sense-act rules
– Stimulus-response
• Advantages:
– Inherently parallel
– No/minimal state
– Very fast
– No memory
• Disadvantages
– No planning
– No learning
Deliberative Systems
• 3 phase model:
– Sense
– Plan
– Act
• Example: Chess
• Advantages:
–?
• Disadvantages:
–?
Deliberative Systems
• 3 phase model:
– Sense
– Plan
– Act
• Advantages:
– can plan
– Can learn
• Disadvantages:
– Needs world model
– Searching and planning are slow
– World model gets outdated
Feedback Control
• React to the sensor changes
• Feedback control == self-regulation
• Q: What type of control system is it?
• Feedback types:
– Positive
– Negative
- and + Feedback
• Negative feedback:
– Regulates the state/output
– Examples: Thermostat, bodies, …
• Positive feedback:
– Amplifies the state/output
– Examples: Stock market
• The first use: ancient Greek water system
• Re-invented in the Renaissance for ovens
W. Grey Walter’s Tortoise
• 1953
• Machina Speculatrix
• Sensors
– 1 photocell,
– 1 bump sensor
• 2 motors
• Reactive control
W. Grey Walter’s Tortoise
Behaviors:
seeking light,
head toward weak light,
 back away from bright
light,
turn and push (obstacle
avoidance),
recharge battery.
Basis for creating adaptive
behavior-based
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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
– productive or unproductive behavior,
adaptation
Ducking
Tortoise behavior
• A path: a
candle on top
of the shell
Tortoise behavior
• Two turtles: Like dancing
New Tortoise
Question
• How does it do the
charging?
– Note: When the
battery is low, it goes
for the light.
Braitenberg Vehicles
• Valentino Braitenberg
– early 1980s
• Extended Walter’s mode
• Based on analog circuits
• Direct connections between
light sensors and motors
• Complex behaviors from very
simple mechanisms
Braitenberg Vehicles
• Complex behaviors from very simple
mechanisms
Braitenberg Vehicles
• 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
• Check: http://www.duke.edu/~mrz/braitenberg/braitenberg.html
• Bots order Styrofoam cubes (16 min 30 sec)
– Tokyo Lecture 3 time 24:30-41:00
Brief History
• 1750: Swiss craftsman create automatons with
clockwork to play tunes
• 1917: Word Robot appeard in Karel Capek’s play
• 1938: Issac Asimov wrote a novel about robots
• 1958: Unimation (Universal Automation) co started
making die-casting robots for GM
• 1960: Machine vision studies started
• 1966: First painting robot installed in Byrne, Norway.
• 1966: U.S.A.’s robotic spacecraft lands on moon.
• 1978: First PUMA (Programmable Universal Assembly)
robot developed by Unimation.
• 1979: Japan introduces the SCARA (Selective
Compliance Assembly Robot Arm).
Early Artificial Intelligence
• "Born" in 1955 at Dartmouth
• "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 execution
Artificial Intelligence
• Early AI had a strong impact on early robotics
• Focused on knowledge, internal models, and
reasoning/planning
• Eventually (1980s) robotics developed more
appropriate approaches => behavior-based and
hybrid control
• AI itself has also evolved...
• Early robots used deliberative control
• Intelligence through construction (5 min 20 sec)
– Tokyo Lecture 2 time 27:40-33:00