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 2025 Course Information

Announcements

Dates/times

Class meetings:

Mondays, Wednesdays, 2:00-3:20PM, NSH 3002

Instructor

Who	Email
Maxim Likhachev

Teaching Assistants

Who	Email
Saudamini Ghatge

Office Hours

Who	Location	Hours
Saudamini	NSH 4222	Tue 11:00-12:00PM
Saudamini	NSH 4222	Thu 3:00-4:00PM
Maxim	NSH 3211	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 with an upper limit on the penalty of 50%. For example, if the assignment is worth 100 points and your score is 80, then your actual score will be 72 if you have 1 additional late day, 64 if you have 2 additional late days, etc.

Class lectures/notes:

Tentative schedule for the class (PDF)

Date	Topic	Slides	Homeworks	Additional Info
1/12 (Mon)	Introduction, What is Robot Planning?	slides	-	-
1/14 (Wed)	Planning Representations: Skeleton-, Grid- and Lattice-based Graphs, Explicit vs. Implicit Graphs	slides	-	-
1/22 (Wed)	*Search Algorithms: Uninformed A Search**	slides	Robot Chasing Target Planning HW1	-
1/27 (Mon)	Search Algorithms: A* Search, Multi-goal A*	slides	-	-
1/29 (Wed)	*Search Algorithms: Heuristics, Backward A, Weighted A* Search**	slides	-	-
2/3 (Mon)	Interleaving Planning and Execution: Anytime Heuristic Search	slides	-	-
2/5 (Wed)	Interleaving Planning and Execution: Incremental Heuristic Search	slides	-	-
2/10 (Mon)	Interleaving Planning and Execution: Real-time Heuristic Search	slides	-	-
2/12 (Wed)	Case Study: Planning for Autonomous Driving	slides	-	-
2/17 (Mon)	Planning Representations: Probabilistic Roadmaps for Continuous Spaces	slides	-	-
2/19 (Wed)	Planning Representations/Search Algorithms: RRT, RRT-Connect, RRT*	slides	N-DoF Planar Arm Planning HW2	-
2/24 (Mon)	*Planning Representations/Search Algorithms: RRT, RRT-Connect, RRT (cont'd)**	-	-	-
2/26 (Wed)	Case Study: Planning for Mobile Manipulators and Articulated Robots	slides	-	-
3/3 (Mon)	SPRING BREAK: NO CLASSES	-	-	-
3/5 (Wed)	SPRING BREAK: NO CLASSES	-	-	-
3/10 (Mon)	Search Algorithms: Markov property, independent vs. dependent variables	slides	-	-
3/12 (Wed)	Case Study: Planning for Coverage, Mapping and Surveyal	slides	-	-
3/17 (Mon)	Planning Representations: Symbolic Representation for Task Planning	slides	-	-
3/19 (Wed)	Presentation of Final Project Proposals	-	Symbolic Planning HW3	-
3/24 (Mon)	Search Algorithms: Planning on Symbolic Representations	slides	-	-
3/26 (Wed)	Planning under Uncertainty: Minimax Formulation	slides	-	-
3/31 (Mon)	Planning under Uncertainty: Expected Value Formulation	slides	-	-
4/2 (Wed)	Planning under Uncertainty: Solving Markov Decision Processes	slides	-	-
4/7 (Mon)	Planning under Uncertainty: Partially-Observable Markov Decision Processes (POMDPs)	slides	-	-
4/9 (Wed)	Exam	-	-	-
4/14 (Mon)	Multi-Robot Planning	slides	-	-
4/16 (Wed)	Multi-Robot Planning (cont'd)	-	-	-
4/21 (Mon)	Learning in Planning	slides	-	-