Learning Implicit Sampling Distributions for Motion Planning
Learning Implicit Sampling Distributions for Motion Planning
复制标题
学习运动规划的隐式采样分布
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Daniel D. Lee
中科院分区：
文献类型：
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作者：
Clark Zhang;Jinwook Huh;Daniel D. Lee
基于采样的运动规划器由于能够有效且均匀地探索状态空间而取得了巨大的成功。然而，对于许多任务来说，不统一地探索状态空间可能会更有效，特别是当存在有关其结构的先验信息时。以前的方法尝试使用手动选择的启发式方法来修改采样分布，该启发式方法可以很好地适用于特定环境，但不适用于普遍情况。在本文中，提出了一种基于策略搜索的方法，作为学习不同环境的隐式采样分布的自适应方法。它利用类似环境中过去搜索的信息在新环境中生成更好的分布，从而降低总体计算成本。我们的方法可以与各种基于采样的规划器结合起来以提高性能。我们的方法在许多任务上得到了验证，包括 7DOF 机器人手臂，与基线方法相比，碰撞检查数量以及扩展的节点数量都有显着改善。
Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficient to not uniformly explore the state space, especially when there is prior information about its structure. Previous methods have attempted to modify the sampling distribution using hand selected heuristics that can work well for specific environments but not universally. In this paper, a policy-search based method is presented as an adaptive way to learn implicit sampling distributions for different environments. It utilizes information from past searches in similar environments to generate better distributions in novel environments, thus reducing overall computational cost. Our method can be incorporated with a variety of sampling-based planners to improve performance. Our approach is validated on a number of tasks, including a 7DOF robot arm, showing marked improvement in number of collision checks as well as number of nodes expanded compared with baseline methods.