基于主动感知经验的结构特征提取

S. Nishide, T. Ogata, R. Yokoya, Kazunori Komatani, H. Okuno, J. Tani
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引用次数: 2

摘要

功能是对象或环境的一个特征,它暗示着如何与之交互。基于能力理论,人类被认为是感知不变结构的认知对象/环境产生的行为。在本文中,作者提出了一种基于人形机器人的物体操作经验,从视觉原始图像中提取物体不变结构的方法。该方法包括两个训练阶段。第一阶段利用带有参数偏差的递归神经网络(RN-NPB)对主动感知过程中提取的动态目标特征进行自组织。第二阶段训练一个附加在RNNPB上的层次神经网络,用于将物体图像和机器人运动与自组织物体特征相关联。对模型的分析揭示了静态对象与动态对象运动(如圆形或稳定)密切相关的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural Feature Extraction Based on Active Sensing Experiences
Affordance is a feature of an object or environment that implies how to interact with it. Based on affordance theory, humans are said to perceive invariant structures for cognizing the object/environment for generating behaviors. In this paper, the authors present a method to extract invariant structures of objects from visual raw images, based on object manipulation experiences using a humanoid robot. The method consists of two training phases. The first phase utilizes Recurrent Neural Network with Parametric Bias (RN-NPB) to self-organize dynamical object features extracted during active sensing with objects. The second phase trains a hierarchical neural network attached to RNNPB for associating object images and robot motions with self-organized object features. Analysis of the model has uncovered static objects features that are closely related to dynamic object motions, such as round or stable.
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