Download - rob102 00 initialization
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Fall 2021 - University of Michigan and Berea College
Robotics 102Introduction to AI and Programming
robotics102.github.io
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Two Action Items for Today
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Two Action Items for Todayhttps://forms.gle/HH6nPQNiNDZMR61z9
Join Robotics 102 Slack Workspace Complete Student Workflow Survey
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First ClassAction items: Join robotics102 slack team, Complete workflow surveyIntroductionsSafety and Masking PoliciesAutonomous navigation is all around! The engine of AI: Graphs and graph algorithmsWe are pioneers for robotics! Course objectives and administrative overviewFlipped classroom: “Hello world!” Lecture will be posted soonUpcoming projects: Project 0 (Calculator) assigned Aug 30, due Sep 20
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Let’s get to know each other
Introductions
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Prof. Chad JenkinsOffice Hours: MW 1-3pm Eastern, Robotics 2236
Michigan Robotics 102 Course Instructor
Faculty in Michigan EECS, PhD 2003 (Univ. Southern California)
Favorite song: Friday Morning by Khruangbin
Best use of robotics: Help care for our aging and disabled populations
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Jana PavlasekOffice Hours:
Michigan Robotics 102 Co-Instructor
4th year doctoral student in Michigan Robotics
Favorite musical artist: Busty and the Bass
Best use of robotics: remote monitoring of our environment and planet
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Your health and wellness is our top priority
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Your health and wellness is our top priority
Proper safety is mandatory
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https://campusblueprint.umich.edu/faqs/
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August 2020
July 2021
August 2021
April 2021
Academic Year 2021-22 ?
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Fall 2021 - University of Michigan and Berea College
Robotics 102Introduction to AI and Programming
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Autonomous Navigation is all around you
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Mobile Manipulation
Passenger Cars
Public Transportation
Logistics
Assistive Tech
Air Mobility
Delivery
Legged Locomotion
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1960 1990 Time1956
First wave AI: Model-based
“Think through the entire problem”
1970 1980 2000 2010
Online route navigation
Self-driving cars
City-scale 3D mapping
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1960 1990 Time1956
First wave AI: Model-based
“Think through the entire problem”
1970 1980 2000 2010
Online route navigation
Self-driving cars
City-scale 3D mapping
Let’s watch this video closely
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Time1956 2011
Second wave AI: Data-driven
“Learn from lots of data”
Rise of
First wave AI: Model-based
“Think through the entire problem”
“deep learning"
AlexNet (2011)
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2011 Time1956
Second wave AI: Data-driven
“Learn from lots of data”
Rational and Robust but Sloooow
First wave AI: Model-based
“Think through the entire problem”
“deep learning"
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2011 Time1956
Second wave AI: Data-driven
“Learn from lots of data”
Rational and Robust but Sloooow
First wave AI: Model-based
“Think through the entire problem”
1958 1989
Waibel et al.“deep learning"
Speech recognition by neural networks
Rosenblatt’s Perceptron
The AI of today needed decades of investment and research
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2011 Time1956
Second wave AI: Data-driven
“Learn from lots of data”
Rational and Robust but Sloooow
First wave AI: Model-based
“Think through the entire problem”
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2011 Time1956
First wave AI: Model-based
“Think through the entire problem”
Second wave AI: Data-driven
“Learn from lots of data”
20??
??
Third wave AI: Explainable
“Combine first and second wave AI to generate explanations”
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2011 Time1956
First wave AI: Model-based
“Think through the entire problem”
Second wave AI: Data-driven
“Learn from lots of data”
AlexNet
Third wave AI: Explainable
“Combine first and second wave AI to generate explanations”
Rational and Robust but Sloooow Accurate and Fast but Delicate
20??
??
Is this handwritten character a ‘9’ or a ‘4’?
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2011 Time1956
First wave AI: Model-based
“Think through the entire problem”
Second wave AI: Data-driven
“Learn from lots of data”
AlexNet
Third wave AI: Explainable
“Combine first and second wave AI to generate explanations”
Rational and Robust but Sloooow Accurate and Fast but Delicate
20??
??
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2011 Time1956
First wave AI: Model-based
“Think through the entire problem”
Second wave AI: Data-driven
“Learn from lots of data”
Third wave AI: Explainable
“Combine first and second wave AI to generate explanations”
20??
??
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2011 Time1956
First wave AI: Model-based
“Think through the entire problem”
Second wave AI: Data-driven
“Learn from lots of data”
20??
??
Third wave AI: Explainable
“Combine first and second wave AI to generate explanations”
Robotics 102 is a first step into modern AI
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2011 Time1956
First wave AI: Model-based
“Think through the entire problem”
Second wave AI: Data-driven
“Learn from lots of data”
20??
??
Third wave AI: Explainable
“Combine first and second wave AI to generate explanations”
Robotics 102 is a first step into modern AI Research for future AI
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Our goal
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Give you the power of autonomous navigation
Our goal
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Give you the power of autonomous navigation
Our goal
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Give you the power of autonomous navigation
Our goal
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Our goalGive you the power of autonomous navigation
Your task
Understand foundational AI algorithms and implement them in code
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Our goalGive you the power of autonomous navigation
“KiMBot” Kiwi Drive MBot
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Our goalGive you the power of autonomous navigation
“MBot Omni” Kiwi Drive MBot
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Robotics 102 Trailer (by Brody!)In the Michigan Robotics Buildinghttps://youtu.be/XN5VHmK1mTM
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Your taskUnderstand foundational AI algorithms
and implement them in code
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
Project 0: Pocket
Calculator
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Understand foundational AI algorithms and implement them in code
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
C++ Programming
Project 0: Pocket
Calculator
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Understand foundational AI algorithms and implement them in code
Project 0: Pocket
Calculator
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithms
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Project 0: Pocket
Calculator
Understand foundational AI algorithms and implement them in code
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithms
A graph is a collection of nodes (noted by circles) that are connected by edges (noted by arrows)
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Understand foundational AI algorithms and implement them in code
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithms
Get First
NumberGet
Operator
Get Next
NumberCompute
Result
Finite State Automata
Project 0: Pocket
Calculator
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Project 0: Pocket
Calculator
Understand foundational AI algorithms and implement them in code
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithms
Get to Wall
Follow Wall
Reactive Controller: Wall follower
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Understand foundational AI algorithms and implement them in code
Project 0: Infix
Calculator
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithms
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Understand foundational AI algorithms and implement them in code
Project 0: Infix
Calculator
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithms
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Project 0: Pocket
Calculator
Understand foundational AI algorithms and implement them in code
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithmsRobot models its environment as graph
of “hidden” nodes with observations
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Project 0: Pocket
Calculator
Understand foundational AI algorithms and implement them in code
Project 1: Wall
following
Project 2: Potential
Fields
Project 3: A*
Pathfinding
Project 4: Neural
Networks
as Graphs and Graph algorithms
Neural network is a graph of artificial neuron nodes that send
activations
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ROB 102 Grading• Final grade determined by total of points earned
• An A grade of some form is earned with 93 points or above
• A B grade of some form is earned with 83 points or above
• A C grade of some form is earned with 73 points or above
Project 0: 15 points
Project 1: 15 points
Project 2: 15 points
Project 3: 15 points
Project 4: 15 points
In Class Activities: 20 points
Participation: 5 points
Advanced Extensions:
4 points
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ROB 102 Flipped Classroom• Course website: robotics102.github.io (or robotics102.org)
• All course material posted on course website, slack workspace, or your git repository
• Robotics 102 does not use Canvas
• Lectures will be posted on course website
• Class lecture meetings (MW 10-11:20am) are dedicated to in-class activities and interactive help on projects
• Class lab sessions (F 12-2pm or F 2-4pm) are dedicated to tutorials for coding and working with robots, as well as interactive help
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Robotics 102 Course Website
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Robotics 102 Course Schedule
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Robotics 102 Project 0: Pocket Calculator
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Us
ROB 102
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We are on the cutting edge
Robotics 102 is for pioneers!
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We are on the cutting edge
Robotics 102 is for pioneers!
This is first offering of an innovative approach to education
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Robotics with Respect
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Robotics with Respect
Inspire from Day 1
Provide opportunity
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Robotics with Respect
Inspire from Day 1
Provide opportunity
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Roboticists show and prove!Time for a demo…
In the new Robotics Building
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Robotics with Respect
Inspire from Day 1
Provide opportunity