Repetition sampling for efficiently planning similar constrained manipulation tasks
Repetition sampling for efficiently planning similar constrained manipulation tasks
复制标题
重复采样以有效规划类似的约束操作任务
DOI：
10.1109/iros.2017.8206116
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Albu
中科院分区：
文献类型：
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作者：
Peter Lehner;A. Albu
我们提出了重复采样，一种新的自适应策略，采样为基础的规划，它提取信息，从以前的解决方案，集中搜索相关的配置空间上的类似任务。我们展示了如何通过从先验解学习高斯混合模型来生成重复采样的分布。我们提出了如何偏置一个基于采样的规划与学习的分布，以产生类似的任务的新路径。我们在一个简单的迷宫中说明了我们的方法，它解释了分布的生成以及重复采样如何在不同的环境中推广。我们将展示如何将重复采样应用于类似的约束操作任务，并提出我们的结果，包括显着的加速执行时间相比，均匀采样。
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.