Learning null space projections
Learning null space projections
复制标题
学习零空间投影
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Vijayakumar
中科院分区：
文献类型：
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作者：
Hsiu;M. Howard;S. Vijayakumar
许多日常人类技能可以从在一系列自我施加的或环境的约束条件下执行某项任务的角度来考虑。近年来，学习和机器人技术领域出现了一些新工具，这些工具能够利用来自受约束和/或冗余系统的数据来揭示潜在的一致性行为，否则这些行为可能会被约束条件所掩盖。然而，尽管已经提出了各种各样关于动作泛化的研究，但很少有研究明确考虑学习运动的约束条件以及应对未知环境的方法。在本文中，我们提出一种学习约束条件的方法，以便一些先前学习到的行为能够以适当的方式适应新环境。特别是，我们考虑学习一个运动学受约束系统的零空间投影矩阵，并研究先前学习到的策略如何能够适应新的约束条件。
Many everyday human skills can be considered in terms of performing some task subject to a set of self-imposed or environmental constraints. In recent years, a number of new tools have become available in the learning and robotics community that allow data from constrained and/or redundant systems to be used to uncover underlying consistent behaviours that may be otherwise masked by the constraints. However, while a wide variety of work for generalisation of movements have been proposed, few have explicitly considered learning the constraints of the motion and ways to cope with unknown environment. In this paper, we propose a method to learn the constraints such that some previously learnt behaviours can be adapted to new environment in an appropriate way. In particular, we consider learning the null space projection matrix of a kinematically constrained system, and see how previously learnt policies can be adapted to novel constraints.