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Chinwe Ekenna, Diane Uwacu, Shawna Thomas, Nancy Amato, "Studying learning techniques in different phases of PRM construction," In Machine Learning in Planning and Control of Robot Motion Workshop (IROS-MLPC), Hamburg, Germany, Oct 2015.
Proceedings(pdf, abstract)

Probabilistic Roadmap Methods (PRMs) solve the motion planning problem in two phases by sampling free configurations and connecting them together to build a map that is used to find a valid path. Existing algorithms are highly sensitive to the topology of the problem, and their efficiency depends on applying them to a compatible problem. Reinforcement learning has been applied to motion planning and rewards the action performed by planners during either sampling or connection, but not both. Previous work computed a global reward and action scheme, which saw a setback when heterogeneous environments were concerned. Local learning (connection) was recently introduced to offset this weakness identified during global learning, and there was some improvement in planner performance. These different learning schemes (global and local) have shown strengths and weaknesses individually. In this paper, we investigate local learning for sampling. We study what type of learning to apply when, and how the two phases of PRM roadmap construction interact, which has not been investigated before. We show the performance using each scheme on a KUKAyouBot, an 8 degree of freedom robot, and analyze what happens when they are all combined during roadmap construction.