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首页> 外文会议>The Fourth joint IEEE international conferences on development and learning and epigenetic robotics >Correcting pose estimates during tactile exploration of object shape: a neuro-robotic study
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Correcting pose estimates during tactile exploration of object shape: a neuro-robotic study

机译：在触觉探索物体形状的过程中纠正姿势估计：神经机器人研究

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摘要

Robots are expected to operate autonomously in unconstrained, real-world environments. Therefore, they cannot rely on access to models of all objects in their environment, in order to parameterize object-directed actions. The robot must estimate the shape of objects in such environments, based on their perception. How to estimate an object's shape based on distal sensors, such as color- or depth cameras, has been extensively studied. Using haptic sensors for this purpose, however, has not been considered in a comparable depth. Humans, to the contrary, are able to improve object manipulation capabilities by using tactile stimuli, acquired from an active haptic exploration of an object. In this paper we introduce a neural-dynamic model which allows to build an object shape representation based on haptic exploration. Acquiring this representation during object manipulation requires the robot to autonomously detect and correct errors in the localization of tactile features with respect to the object. We have implemented an architecture for haptic exploration of an object's shape on a physical robotic hand in a simple exemplary scenario, in which the geometrical models of two different n-gons are learned from tactile data while rotating them with the robotic hand.
机译：机器人有望在不受限制的现实环境中自主运行。因此，他们不能依赖于访问环境中所有对象的模型来参数化面向对象的操作。机器人必须根据物体的感知来估计物体在此类环境中的形状。已经广泛研究了如何基于诸如彩色或深度相机之类的远端传感器来估计物体的形状。然而，尚未在可比较的深度中考虑使用触觉传感器。相反，人类能够通过使用从对物体的主动触觉探索中获得的触觉刺激来改善物体操纵能力。在本文中，我们介绍了一种神经动力学模型，该模型允许基于触觉探索来构建对象形状表示。在对象操纵期间获取此表示要求机器人自动检测并校正相对于对象的触觉特征的定位错误。在一个简单的示例场景中，我们实现了一种结构，用于对物理机械手上的对象的形状进行触觉探索，其中在使用机械手旋转它们的同时，从触觉数据中学习了两个不同n角的几何模型。

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