Office hours: CCB 254 by email or appointment.
Tentative Syllabus Fall 2008 Class Projects Fall 2009 Class Projects For Discussion, Group Building and Collaboration on Projects:Course WikiSummary:
We discuss algorithms for robots and other complex systems that make intelligent decisions in high dimensional or continuous spaces of options. Intelligent decisions take into account both present and future constraints on the system. The course will cover methods for planning with symbolic, numerical, geometric and physical constraints. Topics will range from classical and stochastic planning to continuous robot domains and hybrid control of dynamic systems.
Specific chapters directly relevant to the course will be posted on the website. The readings are not required but they may help you get better background and deeper understanding into the course topics. In fact, if you have time over breaks etc. these are excellent books to read to get a broad appreciation for planning as a field:
"Artificial Intelligence: A New Synthesis," Nilsson
"Artificial Intelligence: A Modern Approach," Russel, Norvig
"Reinforcement Learning" Sutton, Barto
"Principles of Robot Motion", Howie Choset et. al
"Planning Algorithms", Steve LaValle
"Robot Motion Planning", Jean-Claude Latombe
Additional readings from papers will also be posted on the course website when they become relevant. Those will be available to you as PDFs.
Schedule: (PDFs of Slides will be posted During course):
Aug. 23: Introduction, Logic, Situation Calculus: [PDF]
Aug. 25: Advantages of SitCalc, STRIPS Operators, First Planner: [PDF]
Ch. 21-22.1 "Artificial Intelligence: A New Synthesis," Nilsson
Ch. 7.1-7.5, 8.1-9.3, 10.1-10.3 "Artificial Intelligence: A Modern Approach," Russel, Norvig
H. Enderton, "A Mathematical Introduction to Logic," (If you are really curious)
Aug. 30: Properties of State Space Planners: STRIPS: [PDF]
Sept. 1: Shakey, State Space vs. Plan Space: [PDF]
Ch. 22.2 "Artificial Intelligence: A New Synthesis," Nilsson
Ch. 11.1-11.3 "Artificial Intelligence: A Modern Approach," Russel, Norvig
Daniel Weld, An introduction to least-commitment planning. Artificial Intelligence Magazine, 27--61, Winter 1994.[PDF]
Sept. 27: (GUEST LECTURE: Martin Levihn) Roadmap Planning: Visibility vs. Voronoi: [PDF]
Ch. 5.1-5.4 "Principles of Robot Motion," Choset et. al.
Ch. 6.2.3 - 6.2.4 "Planning Algorithms," LaValle
Some Links to Voronoi Construction:
F. Aurenhammer Voronoi diagrams: A survey of a fundamental geometric data structure ACM Computing Surveys, V.23 N.3, 1991. [PDF]
D. Kirkpatrick Efficient Computation of Continuous Skeletons Symposium on Foundations of Computer Science, 1979. [PDF]
Sept. 29: (GUEST LECTURE: Ana Huaman) Navigation Planning: Cells, Grids and Fields: [PDF]
Ch. 6 "Principles of Robot Motion," Choset et. al.
Ch. 6.1 - 6.3 "Planning Algorithms," LaValle
S. Koenig, M. Likhachev: D*Lite. AAAI/IAAI 2002: 476-483 [PDF]
Ch. 3 "Artificial Intelligence: A Modern Approach," Russel, Norvig
Assignments and Projects:
This course has graduate (7649) and undergraduate (4649) sections. Both sections will participate in two group programming projects related to the two covered aspects of planning. The projects will be graded on algorithm implmementation, analysis and results for a total of 40% of the course grade.
Classical Planning (30%)
Motion Planning (30%)
In order to expose students to research in planning, the course will also have a final project that makes up 40% of the grade. This project will involve the design, implementation and validation of a planning algorithm resulting in a conference-style paper and presentation.
7649 Graduate Projects:
Graduate students will work in groups on a project that is relevant to their research goals. The instructor will provide resources such as robot arm/hand hardware and existing algorithms to support this work. Furthermore, students are welcome to use resources or expand on active projects in their own research labs. Final decisions on topics will be made through discussion with the instructor.
4649 Undegraduate Reviews:
Undergraduates can take the role of reviewers for the projects. They will be exposed to research in planning and the peer-review process. Undergrads will be required to review project proposals, final projects, suggest alternative algorithms and find references that back up their claims. They will be graded based on the thoroughness of their reviews, understanding of the project topics and relevance of located references. Undergraduates are given the option to participate in the projects directly and be graded as graduate students.