Hoffmann Heiko
Cognitive Robotics, Max Planck Institute for Human Cognitive and Brain Sciences, Amalienstr. 33, 80799 Munich, Germany.
Neural Netw. 2007 Jan;20(1):22-33. doi: 10.1016/j.neunet.2006.07.003. Epub 2006 Sep 28.
Several scientists suggested that certain perceptual qualities are based on sensorimotor anticipation: for example, the softness of a sponge is perceived by anticipating the sensations resulting from a grasping movement. For the perception of spatial arrangements, this article demonstrates that this concept can be realized in a mobile robot. The robot first learned to predict how its visual input changes under movement commands. With this ability, two perceptual tasks could be solved: judging the distance to an obstacle in front by 'mentally' simulating a movement toward the obstacle, and recognizing a dead end by simulating either an obstacle-avoidance algorithm or a recursive search for an exit. A simulated movement contained a series of prediction steps. In each step, a multilayer perceptron anticipated the next image, which, however, became increasingly noisy. To denoise an image, it was split into patches, and each patch was projected onto a manifold obtained by modelling the density of the distribution of training patches with a mixture of Gaussian functions.
几位科学家提出,某些感知特性基于感觉运动预期:例如,通过预期抓握动作产生的感觉来感知海绵的柔软度。对于空间排列的感知,本文证明了这一概念可以在移动机器人中实现。机器人首先学会预测其视觉输入在运动指令下如何变化。有了这种能力,就可以解决两个感知任务:通过“在脑海中”模拟向障碍物移动来判断前方障碍物的距离,以及通过模拟避障算法或递归搜索出口来识别死胡同。模拟运动包含一系列预测步骤。在每个步骤中,一个多层感知器预测下一幅图像,然而,这幅图像会变得越来越嘈杂。为了对图像去噪,将其分割成小块,然后将每个小块投影到一个流形上,该流形是通过用高斯函数混合对训练小块分布的密度进行建模而得到的。