Robots move: Bootstrapping the development of object representations using sensorimotor coordination

Arren J. Glover, G. Wyeth
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引用次数: 1

Abstract

This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.
机器人移动:利用感觉运动协调引导物体表征的发展
本文研究了融合视觉和运动信息的无监督学习方法。这个问题是针对一个移动机器人提出的,它在逐渐收集数据的过程中发展自己的表示。这个场景是有问题的,因为机器人在每个时间步只有有限的信息,它必须生成和更新它的表示。对象表示被细化为多个感官数据的实例;但是,不确定两个数据实例是否与同一对象同义。这个过程很容易偏离稳定性。提出的工作的前提是,机器人的运动信息煽动成功的视觉表征的产生。对自我运动的理解可以在执行动作之前进行预测,从而增强对数据关联的信念。该系统被实现为数据驱动的部分可观察的半马尔可夫决策过程。对象表示形成为进程的隐藏状态,并通过状态转换与运动命令协调。实验表明,预测过程是使无监督学习方法收敛到一个解决方案的关键-提高精度和召回比单独使用感官数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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