16-350 Planning Techniques for Robotics

Planning is one of the core components that enable robots to be autonomous. Robot planning is responsible for deciding in real-time what the robot should do next, how to do it, where the robot should move next and how to move there. This class does an in-depth study of popular planning techniques in robotics and examines their use in ground and aerial robots, humanoids, mobile manipulation platforms and multi-robot systems. The students learn the theory of these methods and also implement them in a series of programming-based projects.

To take the class students should have a good knowledge of programming and data structures.

Spring 2018 Course Information

Announcements

Dates/times

Class meetings: Mondays, Wednesdays, 1:30-2:50PM, NSH 3002

Instructor

Who Email
Maxim Likhachev

Office Hours

Location Hours
NSH 3211 Fri, 9-10AM & by appointment

Grading

The criteria used to compute the final grade will consist of a combination of scores obtained on the exam, three programming assignments (homeworks), pop quizzes, final project and class participation:

Three homeworks 33%
Exam 20%
In-class pop quizzes 10%
Final project 32%
Participation 5%

Each student has a total of 3 free late days that may be used as needed for homeworks. No late days may be used for the final project!
Additional details: A late day is defined as a 24-hour period after the deadline. After the free late days are used up, each additional late day will incur a 10% penalty on the maximum achievable score. For example, if the assignment is worth 100 points, your maximum score will drop to 90 points for 1 additional late day and to 80 points for 2 additional late days, etc.

Class lectures/notes:

Tentative schedule for the class (PDF)

Date Topic Slides Homeworks Additional Info
1/17 (Wed) Introduction, What is Robot Planning? slides
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1/22 (Mon) Planning Representations: Skeleton- and Grid-based Graphs slides
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1/24 (Wed) Search Algorithms: Uninformed A* Search slides
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1/29 (Mon) Search Algorithms: A* Search slides
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1/31 (Wed) Search Algorithms: Heuristics, Weighted A* Search slides
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2/5 (Mon) NO CLASS -
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2/7 (Wed) Interleaving Planning and Execution: Anytime Heuristic Search slides
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2/12 (Mon) Interleaving Planning and Execution: Incremental Heuristic Search slides
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2/14 (Wed) Interleaving Planning and Execution: Real-time Heuristic Search slides
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2/19 (Mon) Planning Representations: Lattice-based Graphs, Explicit vs. Implicit Graphs slides
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2/21 (Wed) Case Study: Planning for Autonomous Driving slides
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2/26 (Mod) Planning Representations: Probabilistic Roadmaps for Continuous Spaces slides
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2/28 (Wed) Planning Representations/Search Algorithms: RRTs slides
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3/5 (Mon) Case Study: Planning for Mobile Manipulators and Articulated Robots slides
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3/7 (Wed) Search Algorithms: Multi-goal A*, IDA* slides
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3/12 (Mon) SPRING BREAK: NO CLASSES -
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3/14 (Wed) SPRING BREAK: NO CLASSES -
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3/19 (Mon) Case Study: Planning for Coverage, Mapping and Surveyal slides
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3/21 (Wed) Search Algorithms: Markov Property, Dependent Variables slides
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3/26 (Mon) Planning Representations: Symbolic Representation for Task Planning slides
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3/28 (Wed) Search Algorithms: Planning on Symbolic Representations slides
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4/2 (Mon) Presentation of Final Project Ideas -
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4/4 (Wed) Planning under Uncertainty: Minimax Formulation slides
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4/9 (Mon) Planning under Uncertainty: Expected Value Formulation slides
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4/11 (Wed) Planning under Uncertainty: Solving Markov Decision Processes slides
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4/16 (Mon) Planning under Uncertainty: Partially-Observable Markov Decision Processes slides
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4/18 (Wed) Planning under Uncertainty: Partially-Observable Markov Decision Processes (cont'd) Same slides
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