ECE 6504: Advanced Topics in Decision Making for Robotics: Fall 2017

Description

This course will focus on the principles of decision-making in robotics. The motion planning course in Spring '17 focused on the problem of how to go from point A to point B. In this course, we will examine higher-level algorithms that make decisions as to what points A and B should be (for example) depending on the task at hand. Typical tasks that we will use as prototypical examples are where robots as mobile sensors. Specific research topics include:

Combinatorial Vehicle Routing: Traveling Salesperson Problem, Orienteering, Distributed Voronoi Coverage
Information-theoretic path planning: Adaptive sampling, Multi-armed bandit problems, Optimal stopping problems

Submodular optimization, mutual information, entropy, Gaussian processes spatiotemporal modeling

Adversarial planning: Min-max games, Cooperative games, Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs)
Chance-constrained optimization, Reinforcement learning, Deep RL

The first part of the course will consist of background lectures by the instructor. Most of the lectures will be based on foundational papers. This will be followed by an in-depth study of recent research papers which will be led by the students. The students will also work on a research-oriented course project. The goal of the course is to understand and critique research papers, identify open problems, and initiate progress towards solving them. An ideal outcome is for students to write a research paper at the end of the course.

The students will also work in groups on a research-oriented course project. More information about the project is here.

Grading

This is still tentative. Evaluation will be composed of the following:

Course Project (40%)
Paper presentations (20%)
Assignments (20%)
Class participation + scribe (10%)
Paper reviews (10%)

Textbook

No required textbook. Course materials will be drawn from research papers and notes that I will post online.

Schedule

A tentative schedule is here. This will likely evolve during the semester.

Assignments

Details will posted on canvas.

Paper Presentations

Each student will lead the discussion on tbd papers choice during the semester. You may choose a paper from the list given here.

Course Project

The project can be any of the following types:

Survey: An in-depth survey of a research area. The deliverable is a survey paper (between 8-10 pages).
Theoretical: Work on formulating a new research problem and/or solving an existing problem. It is perfectly okay if you cannot solve the problem in its entirety during the class, however, you must demonstrate sufficient effort and progress. The deliverable is a project report (between 8-10 pages).
Implementation: Implement an existing or new algorithm. This can be entirely in software or on hardware. The deliverable is a demo in the last week of classes and a report (6 pages).

All reports must follow the IEEE Transactions format.

Paper Reviews

You will be asked to submit tbd reviews, written individually, for tbd% each. Each review must contain:

a concise, formal description of the problem addressed in the paper;
a list of what you think are the main contributions of the paper over the state-of-the-art;
a list of shortcomings/weaknesses of the paper; and
a list of open problems and possible future work related to the paper.

Prerequisites

This is a seminar-style course that will feature a discussion of research papers. An ability to read and critique research papers is required.

There are no explicit prerequisites. However, students should be very comfortable in at least two of the following areas: Probability, Filtering, and Estimation (ECE 5605/5606); Machine Learning/AI (ECE 4424/4524/5424); Optimization (ECE 5454/5734).

Prior background in robotics is helpful, but not required.

Academic Integrity

You are encouraged to discuss the course materials with the instructor and other students in the class. However, any work that you submit (including but not limited to homeworks, paper reviews, project reports) must be your own. Give proper citations if you use any code or data from anyone else.

The tenets of the Virginia Tech Graduate Honor Code will be strictly enforced in this course, and all assignments shall be subject to the stipulations of the Graduate Honor Code. For more information on the Graduate Honor Code, please refer to the GHS Constitution at this URL.