Robots in Action
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Transcript of Robots in Action
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Lecture 4-1 CS251: Intro to AI/Lisp II
Robots in Action
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Lecture 4-1 CS251: Intro to AI/Lisp II
Announcements
• Feedback response– Late policy (Some credit, helps grading)– Structure of course project (Tyranny of the
majority, grading)– PowerPoint vs. chalk talk: doing the
reading
• Homework assigned today
• Course project descriptions
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Lecture 4-1 CS251: Intro to AI/Lisp II
Asimov’s Three Laws
• A robot may not injure a human being, or, through inaction, allow a human being to come to harm.
• A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
• A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
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Lecture 4-1 CS251: Intro to AI/Lisp II
What’s a Robot?
• Mobile?
• Autonomous
• Softbots
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Lecture 4-1 CS251: Intro to AI/Lisp II
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Lecture 4-1 CS251: Intro to AI/Lisp II
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Lecture 4-1 CS251: Intro to AI/Lisp II
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Lecture 4-1 CS251: Intro to AI/Lisp II
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Lecture 4-1 CS251: Intro to AI/Lisp II
Snips and Snails and Puppy Dog Tails, that’s what robots are made of
• Effectors– Actuators– Degrees of freedom
• Sensors– Proprioception (Looking at your own hand)
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Lecture 4-1 CS251: Intro to AI/Lisp II
Motion for Robots
• Degrees of freedom
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Lecture 4-1 CS251: Intro to AI/Lisp II
Different Sensor, Different Task
• SONAR– Obstacle avoidance
• Lasers– Range-finding
• Vision– Obstacle avoidance– Proprioception
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Lecture 4-1 CS251: Intro to AI/Lisp II
Robot Architecture
• Designing a robot– Common features of many different robots
• Classical
• Nouvelle AI (Situated automata)
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Lecture 4-1 CS251: Intro to AI/Lisp II
Classical (aka SHAKEY)
• Theorem provers proved too general
• No execution monitoring
• Version 2– Specialized programs (LLAs, ILAs)
• Modeling uncertainty
– Learning with macro operators– PLANEX
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Lecture 4-1 CS251: Intro to AI/Lisp II
SHAKEY
• Conclusions– Limited ability to handle unexpected
outcomes
– Each move took 1 hour of computing time• High probability of failure
– STRIPS produced good plans
– Sensory interpretation primitive
From http://hebb.cis.uoguelph.ca/~deb/Robotics/Notes/traditional/page5.html
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Lecture 4-1 CS251: Intro to AI/Lisp II
Situated Automata
• Is classical robotics too difficult?
• Toss out the representation
• Embedded agents– Model the world as interacting automata– Physical environment + Agent– Local state of one = f(Signals from other)– Flakey
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Lecture 4-1 CS251: Intro to AI/Lisp II
Elephants Don’t Play Chess
• What does this mean?
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Lecture 4-1 CS251: Intro to AI/Lisp II
(Physical) Symbol Systems
• Biologically implausible
• Frame problem
• Planning is hard– NP-complete– Heuristics
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Lecture 4-1 CS251: Intro to AI/Lisp II
Physical Grounding
• What’s the hypothesis?
• Evolution– What is Brooks’ argument?
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Lecture 4-1 CS251: Intro to AI/Lisp II
Brooks’ Robots
• Allen
• Tom & Jerry
• Herbert
• Genghis
• Squirt
• Toto
• Seymour
• Gnats
• Ant farm
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Lecture 4-1 CS251: Intro to AI/Lisp II
Subsumption, what is good for?
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Lecture 4-1 CS251: Intro to AI/Lisp II