AI Lab, SoftBank Robotics EU, France.
Univ. Paris 05 Descartes and LPP (CNRS UMR 8158), France.
Neural Netw. 2018 Sep;105:371-392. doi: 10.1016/j.neunet.2018.06.001. Epub 2018 Jun 15.
In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious. For example in the context of artificial perception, this issue is usually circumvented by having engineers pre-define the spatial structure of the problem the agent has to face. We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment. By detecting such compensable experiences the agent can infer the topological and metric structure of the external space in which its body is moving. We propose a theoretical description of the nature of these regularities and illustrate the approach on a simulated robotic arm equipped with an eye-like sensor, and which interacts with an object. Finally we show how these regularities can be used to build an internal representation of the sensor's external spatial configuration.
从基本的感觉运动角度出发,我们根据感觉运动偶然性理论来研究空间感知问题。尽管在我们对世界的感知中空间的概念无处不在,但它的起源在很大程度上仍然是神秘的。例如,在人工感知的背景下,这个问题通常通过让工程师预先定义代理必须面对的问题的空间结构来解决。我们在这里表明,空间结构可以通过一种天真的代理以感觉运动规律的形式自主发现,这些规律对应于所谓的可补偿感觉经验:这些经验既可以由代理本身产生,也可以由其环境产生。通过检测到这些可补偿的经验,代理可以推断出其身体运动的外部空间的拓扑和度量结构。我们提出了这些规律本质的理论描述,并在一个配备类似眼睛的传感器的模拟机器臂上进行了说明,该机器臂与一个物体进行了交互。最后,我们展示了如何使用这些规律来构建传感器外部空间配置的内部表示。