Robots Introduction Based on the lecture by Dr. Hadi Moradi University of Southern California.

Robots Introduction

Based on the lecture by Dr. Hadi MoradiUniversity of Southern California

Outline

• 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• Disadvantages:– Needs world model– Searching and planning are slow– World model gets outdated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

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