16891: Multi-Robot Planning and Coordination

Robotics Institute, Carnegie Mellon University, Spring 2025

Last update: 1-5-2025

More details can be found in course canvas.

Course Introduction

Overview:

The course provides a graduate-level introduction to the field of multi-robot planning and coordination from both AI and robotics perspectives. Topics for the course include multi-robot cooperative task planning, multi-robot path/motion planning, learning for coordination, coordinating robots under uncertainty, etc. The course will particularly focus on state-of-the-art Multi-Agent Path Finding algorithms that can coordinate hundreds of robots with rigorous theoretical guarantees. Current applications for these technologies will be highlighted, such as mobile robot coordination for warehouses and drone swarm control.

Textbook:

There is no assigned textbook for this class. Reference materials are provided in the course schedule as well as the lecture slides.

Course Description:

The course includes lectures, research paper presentations and discussions, and course projects. The majority of this course is a seminar-style survey of issues and approaches to planning and coordination in multi-robot systems. Although the subject area is multi-robot coordination, it is also an explicit goal of this course to advance students’ critical thinking and communication skills, which is achieved through discussions, presentations, and report writing.

Prerequisite knowledge:

There are no formal prerequisites for this class.
Informally, students should be familiar with algorithms and informed search (for example, A*). Students should also have basic knowledge of probability and optimization.

Course topics:

Each of the following 8 topics will be covered by 2-5 lectures:

Course Activities and Grading

  
Paper presentation15%
Paper reading15%
Coding assignments30%
Research project40%

Summary of reading lists and research projects from previous years can be found here: spring 2024, spring 2023.

Tentative Schedule

DateFormatTopics
01/13Lecture 0Overview
01/15Lecture 1Basics of MAPF: A*-based Optimal Methods
01/20Martin Luther King DayNo Class
01/22Lecture 2Basics of MAPF: CBS-based Optimal Methods
01/27Lecture 3Basics of MAPF: CBS-based Bounded-suboptimal Methods
01/29Lecture 4Basics of MAPF: Greedy Search-based Methods
02/03Lecture 5Basics of MAPF: Greedy Rule-based Methods
02/05Lecture 6Task Planning: Multi-Robot Task Allocation
02/10Lecture 7Task Planning: Combined Task and Path Planning
02/12Lecture 8 and Paper Discussion 1Task Planning: Recent Progress (p1,p2)
02/17Lecture 9Motion Planning: Planning and Coordination under Uncertainty
02/19Lecture 10Motion Planning: Planning with Robot Dynamics
02/24Paper Discussion 2Motion Planning: Planning with Robot Dynamics (cont)
02/26Lecture 11Motion Planning: Recent Progress (p3,p4,p5)
03/03Spring BreakNo Class
03/05Spring BreakNo Class
03/10Lecture 12Decentralized Planning: ORCA
03/12Lecture 13Decentralized Planning: Distributed PP and Distributed CSP
03/17Paper Discussion 3Decentralized Planning: Recent Progress (p6,p7,p8)
03/19Lecture 14Lifelong and Online Planning: Task and Path Planning
03/24Lecture 15Lifelong and Online Planning: Interleaving Planning and Execution
03/26Guest Lecture 1TBA
03/31Paper Discussion 4Other Multi-Robot Planning Problems: (p9,p10,p11)
04/02Lecture 16Learning for Planning and Coordination: Overview
04/07Paper Discussion 5Learning for Planning and Coordination: Recent Progress (p12,p13,p14)
04/09Guest Lecture 2TBA
04/14Lecture 17Applications
04/16Lecture 18Applications
04/21Project Presentation 1 
04/23Project Presentation 2