Project Info
Learning to Plan
Bo Wu | bwu@mines.edu
Robot planners offer rich capabilities for autonomous decision-making. However, planners have many variations and parameters to choose from. Selecting the best planning approach is problem-dependent and may require a deep understanding of the planner’s internals. In non-ideal configurations, planning may be slow or not even work. This project will explore techniques to automatically learn and tune the planning
process to obtain the best performance. Possible approaches include machine learning, optimization, and parallelization.
More Information
The Open Motion Planning Library (https://ompl.kavrakilab.org/).
The Task-Motion Kit
(http://www.neil.dantam.name/papers/dantam2018tmkit.pdf).
Grand Engineering Challenge: Not applicable
Student Preparation
Qualifications
Student must be familiar with basic data structures and the C or C++ programming language. Necessary Courses: CSCI-262. Desired Courses: CSCI-358, MATH 213.
Time Commitment
20 hours/month
Skills/Techniques Gained
Student will develop an understanding of robot programming and robot motion planning techniques and libraries. Student will learn various
methods and algorithms for learning and optimization in complex spaces.
Mentoring Plan
Weekly individual meetings with the MURF student and faculty member to discuss progress and provide directed guidance. Weekly lab meetings to discuss the overall project and integration. Assigned graduate student mentor for the MURF student.