CSCI 599 - Mobile Robots and Multi-Robot Systems

CSCI 599
Mobile Robots and Multi-Robot Systems

Instructor Course description Syllabus Course Format Project Grading Related Web sites Presentation Schedule

Taught: Spring 1998 and 1999

If you are enrolled in this class, please check this page regularly to find new or updated information.

Instructor

Prof. Maja J Mataric

Office : SAL 228
Phone  : (213) 740-4520
Email  : mataric@cs.usc.edu
WWW    : http://robotics.usc.edu/~maja
Office
Hours  : by appointment, send email or talk w/ prof. after class

Time and place

Tuesdays and Thursdays, 2:00 - 3:20 PM, in GFS 105.

Course description

This course is a seminar-style hands-on survey of mobile robotics and multi-robot and multi-agent systems. (For photos of the available robots, see the Project section below.)

We will cover the development of the field and overview the different approaches to mobile robot control, including planner-based control, reactive-control, behavior-based control, and hybrid control. We will focus on the issues of resolving the well-known conflict between thinking and acting, i.e., high-level deliberation and low-level real-time control. We will cover different approaches and robot control architectures for addressing this issue. We will also study issues in real-time and active sensing as well as robot learning, both of which are integral to mobile robot control.

In the second part of the course we will discuss scaling up mobile robot control to multi-robot systems and swarms of robots. We will relate this problem to multi-agent control and to Distributed AI, and discuss scaling up of centralized, hierarchical, and decentralized architectures for control. Finally, we will address learning in multi-agent and multi-robot systems, and deal with the issues of interference and credit assignment.

Syllabus and Reading List

Syllabus format:
Date of class session: Topics being discussed

Assigned readings being presented and discussed.

Jan 8 : Introduction and overview of the course; intro to the available robot testbeds

Jan 9 : A lab introduction to the R1 and Geo robots, presented by Gaurav Sukhatme
and Barry Werger; 1pm, SAL 103.

Jan 13 : Survey of basic control methods

Ch 2 and Ch 25 (773-781, 786-790 only) from "AI, a Modern Approach" by Russell & Norvig

a review of "Intelligence Without Reason" by R. Brooks (1991)

Jan 15 : Survey of sensors and effectors; sensor fusion and fault tolerance
Guest lecturer: Gaurav Sukhatme

Ch 24 (724-756 only) from "AI, a Modern Approach" by Russell & Norvig

parts of "Survey of Collision Avoidance and Ranging Sensors for Mobile Robots" by R. Everett (1988)

"Biological and Cognitive Foundations of Intelligent Sensor Fusion" by R. Murphy (1996)

Jan 20 : Reactive systems

"What are Plans For" by P. Agre and D. Chapman (1990)

"Elephants Don't Play Chess" by R. Brooks (1990)

"A Robust Layered Control System for a Mobile Robot" by R. Brooks (1986)

Jan 22 : Reactive systems

"Universal Plans for Reactive Robots in Unpredictable Environments" by M. Schoppers (1987)

"Action and Planning in Embedded Agents" by L. Kaelbling & S. Rosenschein (1990)

Jan 27 : Hybrid systems

"SSS: A Hybrid Architecture Applied to Robot Navigation" by J. Connell (1992)

"On Three-Layer Architectures" by E. Gat (1998)

Jan 29 : Hybrid systems

"Symbol Grounding via a Hybrid Architecture in an Autonomous Assembly System" by
C. Malcolm and T. Smithers (1990)

"AuRA: Principles and Practice in Review" by R. Arkin and T. Balch (1997)

Feb 3 : Behavior-based systems

"Internalized Plans: A Representation for Action Resources" by D. Payton (1990)

"Behavior-Based Control: Examples from Navigation, Learning, and Group Behavior" by M. Mataric (1997)

Feb 5 : Behavior-based systems

"Motor Schema Based Navigation for a Mobile Robot: An Approach to Programming by Behavior" by R. Arkin (1987)

"Situated Agents Can Have Goals" by P. Maes (1990)

Feb 10 : Reinforcement Learning (RL) in Robotics

"Learning to Coordinate Behaviors" by P. Maes and R. Brooks (1990)

"Automatic Programming of Behavior-Based Robots Using Reinforcement Learning" by J. Connell and S. Mahadevan (1990)

Feb 12 : Supervised Learning in Robotics

"ALVINN: An Autonomous Land Vehicle in A Neural Network" by D. Pomerleau (1992)

"Rapidly Adapting Artificial Neural Networks for Autonomous Navigation" by D. Pomerleau (1992)

"Model-based Learning for Mobile Robot Navigation from the Dynamical Systems Perspective" by J. Tani (1996)

Feb 17 : Reinforcement Learning in Robots/Agents

"Learning to use selective attention and short-term memory in sequential tasks" by A. McCallum (1996)

parts of "Hidden State and reinforcement learning with instance-based state identification" by McCallum et al (1996)

"Rapid, Safe, and Incremental Learning of Navigation Strategies" by J. Millan (1996)

Feb 19 : Genetic/Evolutionary Approaches to Robot Learning

"Evolving 3D Morphology and Behavior by Competition" by K. Sims (1994)

"Evolving Electronic Robot Controllers that Exploit Hardware Resources" by A. Thompson (1995)

Feb 24 : Genetic/Evolutionary Approaches to Robot Learning

"RoboShepherd: Learning a Complex Behavior" by A. Schultz et al (1996)

"Evolution of Subsumption Using Genetic Programming" by J. Koza (1992)

Feb 26 : Multi-agent control (graphical agents)

"Flocks, Herds, and Schools: A Distributed Behavioral Model" by C. Reynolds (1987)

"Artificial Fishes with Autonomous Locomotion, Perception, Behavior and Learning in a Simulated Physical World" by D. Terzopoulos et al (1994)

Mar 3 : Summary of control and learning; Quiz

Mar 5 : Multi-robot control

"From Local Actions to Global Tasks: Stigmergy and Collective Robotics" by R. Beckers et al (1984)

"Swarm-Made Architectures" by J. L. Deneubourg et al (1991)

"The Dynamics of Collective Sorting: Robot-like Ants and Ant-like Robots" by J. L. Deneubourg et al (1990)

Mar 10 : No class: Spring Break

Mar 12 : No class: Spring Break

Mar 17 : Multi-agent/robot control

"Principles of Minimal Control for Comprehensive Team Behavior" by B. Werger (1998)

"Task Modeling in Collective Robotics" by C. Kube and H. Zhang (1997)

Mar 19 : Multi-agent control

"Group Behaviors for Systems with Significant Dynamics" by D. Brogan and J. Hodgins (1997)

"Decentralized Motion Planning for Multiple Mobile Robots: The Cocktail Party Model" by V. Lumelsky and K. Harinarayan (1997)

Mar 24 : Guest Speaker and Videos

Mar 26 : Biologically-Inspired Models

"A Biological Perspective on Autonomous Agent Design" by R. Beer (1990)

"Robotic Experiments in Cricket Phonotaxis" by B. Webb (1994)

Mar 31 : Multi-agent/robot learning

"Multiagent Systems: A Survey from a Machine Learning Perspective" by P. Stone and M. Veloso (1997)

"Reinforcement Learning in the Multi-Robot Domain" by M. Mataric (1997)

Apr 2 : Multi-agent/robot learning

"Phylogenetic and Ontogenetic Learning in a Colony of Interacting Robots" by A. Agah and G. Bekey (1997)

"Agents That Learn from Other Competitive Agents" by M. Asada et al (1995)

"Evolving Behavioral Strategies in Predators and Prey" by T. Haynes and S. Sen (1996)

Apr 7 : Philosophical Overviews

"Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics" by N. Jakobi et al (1997)

"Modeling Adaptive Autonomous Agents" by P. Maes (1994)

Apr 9 : Philosophical Overviews

"The Artificial Life Roots of Artificial Intelligence" by L. Steels (1994)

"Why Better Robots Make it Harder" by T. Smithers (1994)

Apr 14 : Overview of the material

"Intelligence Without Reason" by R. Brooks (1991)

Apr 16 : No class, project preparation

Apr 21 : Project presentations and discussion
2-3:30pm, location: SAL 322

Apr 24 : Project presentations and discussion
4-6pm, location: SAL 322

Apr 28 : Project presentations and discussion
2-4pm, location: EEB 349

Class format

This course consists of reading, presenting, critiquing, and discussing papers, and implementing and presenting a hands-on project employing the ideas covered in the class. No textbook is used; instead, we will read original papers from the field, and discuss and critique them in class. Students will take turns presenting and debating the readings.

Helpful information on how to present the readings in class can be found here.

Course Project

The students will apply the principles learned in class on (one or more) physical mobile robot(s) or on a sophisticated simulation:

1) Information on robot kits that you can purchase is listed below, under Related Web Sites.

2) We are making Geo II, a high-degree-of-freedm legged robot, available for your project use; it is shown below on the left. For more information, look here.

3) We are also providing access to some R1 research robots in exchange for help with hardware development. These robots are shown on the right; for more information, look here. However, the robots are currently undergoing redesign and will require work before being useful.

4) Information on some simulation testbeds is also listed below, under Related Web Sites.

Project topics and platforms must be approved by Prof. Mataric.

A detailed final paper is a required part of the final project; it will be due on the last day of presentations, and it will be made available to any interested students in the class.

Helful information for preparing your project presentation can be found here.

Helful information for preparing your final paper can be found here.

Grading

Performance in the class will be based on participation in class discussions, presentations of the readings, a quiz reviewing the basic concepts covered in the class, and a final project (with an associated paper and demonstration). Regular attendance and participation is expected; if you need to miss a class, please speak to Prof. Mataric ahead of time.

Specifically, final course grades will consist of:

15%   quiz
20%   in-class discussion participation
30%   in-class paper presentations 
35%   final project implementation and final paper

Related Web Sites and Readings

Robot Kits:

The Mondo-tronics Robot Store. Go to "Robot Store Web Order Catalog" then go to "Programmable Robots"; the others may be cool but cannot be programmed and cannot have sensors added to them. Also check out "Wheeled Platforms".

Johuco Ltd. Four different cheap, versatile, and programmable robot kits by Jonathan Connell.

Information on implementing a neural controller for a mobile robot by using a robot kit, by Jonathan Connell (Johuco, Ltd.)

Information on designing behavior-based robots , tied into a robot kit, by Jonathan Connell (Johuco, Ltd.).

Information on Lynxmotion , a series of inexpensive mobile robot, manipulator and sensor kits .

Information on the Rug Warrior , the robot kit that comes with a book: "Mobile Robots, Inspiration to Implementation" by J. L. Jones and A. M. Flynn, published by A. K. Peters, MA. Rug warrior technical information is also available, including schematics, etc. Rug Warrior kits are quite popular so you can find people who have used them to implement interesting robots. For example see Franz, implemented by Francois Michaud.

For information on obtaining the MIT kits (previously used in the USC CSCI499 course), email fredm@media.mit.edu

Information on KIPR, the KISS Institute for Practical Robotics.

All Electronics, a source of affordable sensors and other components. LA store: (213) 380-8000.

Diversified Enterprises Web page with educational and robot kit information.

The IEEE Computer Society Web page with links to "Stiquito: advanced experiments with a simple and inexpensie robot" textbook and kit by J. Conrad and J. Mills.

Simulation Testbeds:

Public domain software package of a mobile robot Khepera simulator written by Olivier Michel. You can program the simulated robot in C or C++, then test it in a simulated environment using the X11 graphical interface on a Unix system.

Public domain software for the official RoboCup robot soccer simulator. The page is full of useful information. People who definitely do not want to use physical robots can use this simulation to explore issues of multi-agent coordination, cooperation, and learning for the course project.

Public domain software for a Java-based soccer simulation called JavaSoccer, written by Tucker Balch.

A public domain Lisp-based simulation of Valentino Braitenberg's Vehicles.

Tips for Preparing Presentations:

Look here for info on how to prepare presentations of the readings.

Look here for info on how to prepare your final project presentation.

Paper authors' Web pages:

Arvin Agah, Phil Agre, Ron Arkin
Tucker Balch, Randy Beer
George Bekey, David Brogan, Rod Brooks
Jon Connell
Erann Gat
Thomas Haynes, Jessica Hodgins
Nick Jakobi
Leslie Kaelbling, John Koza, Ron Kube
Vladimir Lumelsky
Pattie Maes, Sridhar Mahadevan, Maja Mataric, Andrew McCallum
Dean Pomerleau
Craig Reynolds, Stan Rosenschein
Alan Schultz, Sandip Sen, Karl Sims, Luc Steels, Gaurav Sukhatme
Jun Tani, Demetri Terzopoulos
Adrian Thompson
Barbara Webb, Barry Werger

A few relevant books:

Artificial Intelligece, A Modern Approach Russel & Norvig, Prentice Hall.
Mobile Robots, Inspiration to Implementation, Jones & Flynn, A. K. Peters.
Behavior-Based Robotics, Arkin, MIT Press, 1998.
Neural Networks and Fuzzy Systems, Kosko, Prentice Hall.
Perceptrons (expanded edition), Minsky and Papert, MIT Press.
Introduction to the Theory of Neural Computing, Hertz, Krogh & Palmer, Addison Wesley.
Genetic Algorithms in Search, Optimisation, and Machine Learning, Goldberg, Addison Wesley.
Artificial Life, Langton, Addison Wesley.

Links to other mobile and multi-robot pages:

USC Robotics Lab Mobile and Multiple Robotics Links Page
USC Robotics Lab Multi-Agent Systems and DAI Links Page
Honda's walking robot.

(Selected) Relevant Conferences and Journals:

Robotics:
Autonomous Robots
IEEE Transactions on Robotics and Automation
International Journal of Robotics Research
Robotics and Autonomous Systems
IEEE Transactions on Man, Systems, and Cybernetics
International Conference on Robotics and Automation (ICRA)
IEEE/RSJ International Conference on Intelligent Robotic Systems (IROS)

Adaptive Biological and Artificial Behavior:
Adaptive Behavior Journal
Simulation of Adaptive Behavior Conference (SAB)
Cognitive Science Journal
Cognitive Science Conference

Learning:
Machine Learning Journal
International Conference on Machine Learning (ICML)

Artificial Intelligence:
Artificial Intelligence Journal
Journal of Artificial Intelligence Research
Journal of Experimental and Theoretical AI
American Association for Artificial Intelligence Conference (AAAI)
International Joint Conference on AI (IJCAI)

Autonomous Agents:
Autonomous Agents Journal
Autonomous Agents Conference (Agents)

Artificial Life:
Artificial Life Journal
Artificial Life Conference (Alife)
European Artificial Life Conference (ECAL)

Computational Neural Network Approaches:
Neural Computation
Neural Information Processing Systems Conference (NIPS)

Presentation Schedule

Date	presenter	presenter	presenter
1/8	Maja Mataric	Barry Werger	Gaurav Sukhatme
1/13	---	Maja Mataric
---
1/15	---	Gaurav Sukhatme	---
1/20	Ilia Ovsiannikov	Wilko Hein	Aswath Mohan
1/22	Richard Kolodji	Les Williams	---
1/27	Brian Ellenberger	Göksel Dedeoglu	---
1/29	Tony Mactutis	Srinivasan Lakshmanan	---
2/3	Pavlin Radoslavov	Juan Francisco Lopez	---
2/5	Brian Ellenberger	Les Williams	---
2/10	Tony Mactutis	Richard Kolodji	---
2/12	Aswath Mohan	Aswath Mohan	Göksel Dedeoglu
2/17	Göksel Dedeoglu	Göksel Dedeoglu	Srinivasan Lakshmanan
2/19	Wilko Hein	Wilko Hein	---
2/24	Pavlin Radoslavov	Sean Morrow	---
2/26	Sean Morrow	Tony Mactutis	---
3/3	Review	Quiz	---
3/5	Pavlin Radoslavov	Juan Francisco Lopez	Srinivasan Lakshmanan
3/17	Pavlin Radoslavov	Aswath Mohan	---
3/19	Les Williams	Wilko Hein	---
3/24	Guest	speaker	Videos
3/26	Brian Ellenberger	Juan Francisco Lopez	---
3/31	Aswath Mohan	Sean Morrow	---
4/2	Juan Francisco Lopez	Tony Mactutis	Les Williams
4/7	Richard Kolodji	Sean Morrow	---
4/9	Brian Ellenberger	Srinivasan Lakshmanan	---
4/14	Richard Kolodji	course	review
4/21	course Brian Ellenberger	project Pavlin Radoslavov	presentations Aswath Mohan
4/24	course Göksel Dedeoglu	project Richard Kolodji	presentations Tony Mactutis Les Williams
4/28	course Wilko Hein	project Juan Francisco Lopez	presentations Srinivasan Lakshmanan

Pre-requisite

Undergraduate AI (e.g., CSCI 460) or permission of instructor.

U.S.C. Computer Science Department
U.S.C.
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CSCI 599 Mobile Robots and Multi-Robot Systems