Active audition for robots using parameter-less self-organising maps
Active audition for robots using parameter-less self-organising maps
复制标题
使用无参数自组织映射对机器人进行主动试镜
DOI：
10.14264/158247
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
E. Berglund
中科院分区：
文献类型：
--
作者：
E. Berglund
机器人如何才能感知周围的环境呢？它如何创造自己对真实世界的主观的、内在的表现，以便一个人的关系反映在另一个人身上？众所周知，类似于自组织映射(SOM)的结构在动物中参与了这一任务，本文致力于探索类似的方法是否以及如何成功地应用于机器人学。为了在定向学习和内置设计假设的最小指导下研究环境到抽象的映射，本文研究了主动听觉任务，在该任务中，系统必须确定声源的方向并朝向它，无论是水平方向还是垂直方向。以前对动物定向听力的解释，以及机器人定向听力算法的实现，往往集中在两个最著名的定向线索上：强度和时差。这篇论文假设，使用更广泛的度量指标，即相位和相对强度差的协同作用是有利的。提出了一种基于无参数自组织映射算法(PLSOM)的主动试听算法。PLSOM用于从高维输入空间到低维输出空间提取模式。在此应用中，输出空间被映射到正确的电机命令，用于通过过滤不需要的噪声来转向信号源并将注意力集中在选定的信号源上。PLSOM的降维能力使其能够使用不止两个方向线索来计算方向。本文提出了一种新的用于SOM训练的PLSOM算法，并对其相对于普通SOM算法的性能进行了量化。证明了PLSOM的数学正确性，并检验了这种新算法的性质和一些应用，特别是在以函数形式自动建模机器人环境：逆运动学(IK)方面。IK问题原则上与主动试听问题有关--功能性的而不是抽象的现实表征--但提出了一些新的问题，即如何在计划和执行动作时使用这种内部表征。PLSOM还应用于高维数据分类和无模型混沌时间序列预测。设计了一种基于Q-学习的强化学习算法，并对其进行了测试。这个变种解决了与随机报酬函数有关的一些问题。给出了正确的状态-作用配对的数学证明。
How can a robot become aware of its surroundings? How does it create its own subjective, inner representation of the real world, so that relationships in the one are reflected in the other? It is well known that structures analogous to Self-Organising Maps (SOM) are involved with this task in animals, and this thesis undertakes to explore if and how a similar approach can be success- fully applied in robotics. In order to study the environment-to-abstraction mapping with a minimum of guidance from directed learning and built-in design assumptions, this thesis examines the active audition task in which a system must determine the direction of a sound source and orient towards it, both in horizontal and vertical direction. Previous explanations of directional hearing in animals, and the implementation of directional hearing algorithms in robots have tended to focus on the two best known directional clues; the intensity and time differences. This thesis hypothesises that it is advantageous to use a synergy of a wider range of metrics, namely the phase and relative intensity difference. A solution to the active audition problem is proposed based on the Parameter- Less Self-Organising Map (PLSOM), a new algorithm also introduced in this thesis. The PLSOM is used to extract patterns from a high-dimensional input space to a low-dimensional output space. In this application the output space is mapped to the correct motor command for turning towards the source and focusing attention on the selected source by filtering unwanted noise. The dimension-reducing capability of the PLSOM enables the use of more than just two directional clues for computation of the direction. This thesis presents the new PLSOM algorithm for SOM training and quantifies its performance relative to the ordinary SOM algorithm. The mathematical correctness of the PLSOM is demonstrated and the properties and some applications of this new algorithm are examined, notably in automatically modelling a robot's surroundings in a functional form: Inverse Kinematics (IK). The IK problem is related in principle to the active audition problem - functional rather than abstract representation of reality - but raises some new questions of how to use this internal representation in planning and execution of movements. The PLSOM is also applied to classification of high-dimensional data and model-free chaotic time series prediction. A variant of Reinforcement Learning based on Q-Learning is devised and tested. This variant solves some problems related to stochastic reward functions. A mathematical proof of correct state-action pairing is devised.