Learning to recognize objects through curiosity-driven manipulation with the iCub humanoid robot
Learning to recognize objects through curiosity-driven manipulation with the iCub humanoid robot
复制标题
使用 iCub 人形机器人通过好奇心驱动的操作学习识别物体
DOI：
10.1109/devlrn.2013.6652525
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Pierre
中科院分区：
文献类型：
--
作者：
S. Nguyen;S. Ivaldi;Natalia Lyubova;Alain Droniou;Damien Gérardeaux;David Filliat;V. Padois;Olivier Sigaud;Pierre
在本文中，我们解决了在发展机器人场景中通过操纵来学习识别物体的问题。从终身学习的角度来看，人形机器人应该能够通过主动感知来提高其对物体的知识。我们的方法源于婴儿的认知发展，利用主动的好奇心驱动的操作来提高对物体的感知学习。这些功能被实现为感知、控制和主动探索模块，作为MACSi项目认知架构的一部分。在本文中，我们将这些功能集成到一个主动感知系统中，该系统通过操纵来学习识别物体。我们在本文中的工作集成了自下而上的视觉系统、复杂机器人系统的控制系统和自上而下的交互式探索方法，主动选择一种探索方法来收集数据，以及与人类互动是否有利可图。实验结果表明，仿人机器人iCub通过选择适当的探索策略来提高能力进步，并将精力集中在最复杂的任务上，可以通过操纵和与教师的互动来学习识别3D物体。因此，学习者可以通过主动自我调节其请求帮助的方式与人类进行互动学习。
In this paper we address the problem of learning to recognize objects by manipulation in a developmental robotics scenario. In a life-long learning perspective, a humanoid robot should be capable of improving its knowledge of objects with active perception. Our approach stems from the cognitive development of infants, exploiting active curiosity-driven manipulation to improve perceptual learning of objects. These functionalities are implemented as perception, control and active exploration modules as part of the Cognitive Architecture of the MACSi project. In this paper we integrate these functionalities into an active perception system which learns to recognise objects through manipulation. Our work in this paper integrates a bottom-up vision system, a control system of a complex robot system and a top-down interactive exploration method, which actively chooses an exploration method to collect data and whether interacting with humans is profitable or not. Experimental results show that the humanoid robot iCub can learn to recognize 3D objects by manipulation and in interaction with teachers by choosing the adequate exploration strategy to enhance competence progress and by focusing its efforts on the most complex tasks. Thus the learner can learn interactively with humans by actively self-regulating its requests for help.