Information specific to this year is available on the Wiki: Course Wiki + ProjectsSummary:
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.
This course has graduate (8803) and undergraduate (4803) sections. Both sections will participate in three group programming projects related to the three covered aspects of planning. The projects will be graded on algorithm implmementation, analysis and results for a total of 50% of the course grade.
Classical Planning (25%)
Motion Planning (25%)
In order to expose students to research in planning, the course will also have a final project that makes up 50% of the grade. This project will involve the design, implementation and validation of a planning algorithm resulting in a conference-style paper and presentation.
Robot arm planning and control.
Planning with abstractions.
Planning for a novel balancing platform.
Grasp planning for a robot hand.
Planning or control projects relevant to ongoing research.
8803 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.
4803 Undegraduate Reviews:
Undergraduates will 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.