Repetition sampling for efficiently planning similar constrained manipulation tasks

Abstract

We present repetition sampling, a new adaptive strategy for sampling based planning, which extracts information from previous solutions to focus the search for a similar task on relevant configuration space. We show how to generate distributions for repetition sampling by learning Gaussian Mixture Models from prior solutions. We present how to bias a sampling based planner with the learned distribution to generate new paths for similar tasks. We illustrate our method in a simple maze which explains the generation of the distribution and how repetition sampling can generalize over different environments. We show how to apply repetition sampling to similar constrained manipulation tasks and present our results including significant speedup in execution time when compared to uniform sampling.