Active Categorical Perception of Object Shapes in a Simulated Anthropomorphic Robotic Arm
Active Categorical Perception of Object Shapes in a Simulated Anthropomorphic Robotic Arm
复制标题
模拟拟人机械臂中物体形状的主动分类感知
DOI：
10.1109/tevc.2010.2046174
复制
发表时间：
2010
影响因子：
14.3
通讯作者：
S. Nolfi
中科院分区：
文献类型：
--
作者：
E. Tuci;Gianluca Massera;S. Nolfi
主动知觉指的是一种研究知觉的理论方法，其基础是知觉是一种行为方式，而不是大脑构建世界的内部表征的过程。主动感知的工作原理可以通过建立基于机器人的模型来有效地测试，在该模型中，感知类别之间的关系和身体与环境的相互作用可以被实验地操纵。在本文中，我们研究了一项任务中的触觉知觉机制，该任务要求配备粗粒度触觉传感器的神经控制拟人机械臂对球形和椭圆形物体进行感知分类。我们表明，通过人工进化技术合成的最好的个体，发展出一种近乎最佳的辨别物体形状的能力，以及在新环境中概括其技能的能力。结果表明，智能体通过动作自我选择所需的信息，并随着时间的推移整合经验的感觉-运动状态，以一种有效而稳健的方式解决分类任务。
Active perception refers to a theoretical approach to the study of perception grounded on the idea that perceiving is a way of acting, rather than a process whereby the brain constructs an internal representation of the world. The operational principles of active perception can be effectively tested by building robot-based models in which the relationship between perceptual categories and the body-environment interactions can be experimentally manipulated. In this paper, we study the mechanisms of tactile perception in a task in which a neuro-controlled anthropomorphic robotic arm, equipped with coarse-grained tactile sensors, is required to perceptually categorize spherical and ellipsoid objects. We show that best individuals, synthesized by artificial evolution techniques, develop a close to optimal ability to discriminate the shape of the objects as well as an ability to generalize their skill in new circumstances. The results show that the agents solve the categorization task in an effective and robust way by self-selecting the required information through action and by integrating experienced sensory-motor states over time.
影响因子：
64.8
作者：
DILL, M;WOLF, R;HEISENBERG, M
通讯作者：
HEISENBERG, M