Students in 16.412 complete two projects. For the 'Advanced Lecture', students research a topic in the field of cognitive robotics, and deliver a lecture in-class on their selection. For the 'Final Project', teams expand the field through innovative methods or applications. Students report on the project through individual papers and a brief team presentation.

Advanced Lecture Information

The following advanced lecture summary is excerpted, in part, from the Advanced Lecture Proposal Guidelines (PDF).

Objectives

The purpose of the advanced lecture is to identify one to two advanced research methods, and to present these clearly and pedagogically, through a 45 minute oral presentation, through a written tutorial, and in the case of a three person team, with a demonstration. An additional objective is to learn to develop an understanding of the literature in a collaborative context, through one to two partners.

Advanced lectures improve the class' awareness of current research in the field of cognitive robotics. The instructors often re-use them in future semesters to keep the course up to date. A list of suggested topics is linked at the bottom of this section.

Grading

Advanced lectures are prepared by students and presented during scheduled lecture sessions. Students perform a 'dry-run' in advance of their in-class presentation. The dry-run is an opportunity for students to exchange feedback on their presentations. The revised lecture, delivered in-class, is subject to the following scoring scheme:

Scoring Guidelines for Technical Presentations (PDF)

Guidelines

In previous semesters, this project was introduced through a sequence of assignments: a warm-up literature review, and an advanced lecture proposal. Although the warm-up exercise was not assigned in Spring 2004, it introduces the advanced lecture project itself, which was undertaken by all students this semester.

Advanced Lecture Warm-Up Exercise (PDF)

Advanced Lecture Proposal Guidelines (PDF)

Advanced Lecture Submission Guidelines (PDF)

Final Project Information

The following final project summary is excerpted, in part, from the Final Project Guidelines (PDF).

Objectives

The purpose of the project is to develop a deep understanding of one or two methods for creating cognitive robots and intelligent embedded systems, and to innovate upon these methods, to lend novel insight into their behavior through analysis or to apply the method in an innovative manner.

More specifically, you should demonstrate the ability to:

• Clearly state and motivate an interesting, focused innovation to intelligent embedded systems. An innovation may be an important analytical question, a novel algorithmic extension or an innovative application.
• Extract and evaluate the relevant literature using the web and library resources.
• Provide a simple explanation for the algorithms used in your project, using pedagogical examples to highlight key features of the algorithm.
• If a design project, describe the design of the intelligent embedded systems you are creating and the rationale for the method applied in the context of the project. If this is an analysis project, then described the experimental method that you are pursuing.
• Implement and demonstrate an algorithm or application in support of your project goals.
• Evaluate the approach analytically and/or empirically.

Grading

• A - represents mastery: the ability to analyze and extend existing methods in a way that is novel and insightful; the ability to explain and motivate in a manner that is particularly intuitive.
• B - represents solid competence: the ability to articulately motivate, explain, implement and evaluate a focused set (i.e., 1 or 2) of intelligent embedded systems methods.
• C - represents partial competence of the above.

Report Guidelines

The report should reveal a depth of understanding. It should communicate the objectives, core description, developments and results of your project. Three main elements to present in your report:

1. Articulation of the set of methods upon which your project is built, in a pedagogical (tutorial-like) manner.
2. Empirical and/or analytical evaluation and insights.
3. Innovation that involves applying and/or extending methods in a novel way.

Presentation Guidelines

Each team gives a presentation (approximately 5 minutes / group member). Selected student work from Spring 2004 is linked in the table below. A list of suggested topics is linked at the bottom of this section.

STUDENTS PROJECTS Alexander Omelchenko Autonomous Visual Tracking Algorithms: Presentation Report Lars Blackmore Steve Block Cooperative Q Learning: Presentation Report by Block Report by Blackmore Seung H. Chung Robert T. Effinger Thomas Léauté Steven D. Lovell Model-based Programming for Cooperating Vehicles: Presentation Report by Léauté (PDF - 1.3 MB) (Courtesy of Thomas Leaute. Used with permission.) Report by Chung (PDF) (Courtesy of Seung Chung. Used with permission.) Report by Effinger (PDF) (Courtesy of Robert Effinger. Used with permission.) Report by Lovell Morten Rufus Blas Søren Riisgaard SLAM for Dummies: Presentation (PDF - 1.4 MB) (Courtesy of Morten Blas and Soren Riisgaard. Used with permission.) Report by Blas (PDF) (Courtesy of Morten Blas. Used with permission.) Report by Riisgaard (PDF) (Courtesy of Soren Riisgaard. Used with permission.) Vikash K. Mansinghka Towards Visual SLAM in Dynamic Environments: Presentation (PDF) (Courtesy of Vikash Mansinghka. Used with permission.) Report (PDF) (Courtesy of Vikash Mansinghka. Used with permission.)

Suggested Topics and Readings for the Advanced Lecture and Project