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首页> 外文会议>The Fourth joint IEEE international conferences on development and learning and epigenetic robotics >Learning intermediate object affordances: Towards the development of a tool concept
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Learning intermediate object affordances: Towards the development of a tool concept

机译：学习中间对象能力：朝工具概念发展

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Inspired by the extraordinary ability of young infants to learn how to grasp and manipulate objects, many works in robotics have proposed developmental approaches to allow robots to learn the effects of their own motor actions on objects, i.e., the objects affordances. While holding an object, infants also promote its contact with other objects, resulting in object-object interactions that may afford effects not possible otherwise. Depending on the characteristics of both the held object (intermediate) and the acted object (primary), systematic outcomes may occur, leading to the emergence of a primitive concept of tool. In this paper we describe experiments with a humanoid robot exploring object-object interactions in a playground scenario and learning a probabilistic causal model of the effects of actions as functions of the characteristics of both objects. The model directly links the objects' 2D shape visual cues to the effects of actions. Because no object recognition skills are required, generalization to novel objects is possible by exploiting the correlations between the shape descriptors. We show experiments where an affordance model is learned in a simulated environment, and is then used on the real robotic platform, showing generalization abilities in effect prediction. We argue that, despite the fact that during exploration no concept of tool is given to the system, this very concept may emerge from the knowledge that intermediate objects lead to significant effects when acting on other objects.
机译：受年幼婴儿学习如何抓握和操纵物体的非凡能力的启发，许多机器人技术著作提出了发展性方法，以使机器人能够学习自己的动作对物体的影响，即物体的承受能力。婴儿在握住物体时，也会促进其与其他物体的接触，从而导致物体与物体之间的相互作用，从而产生其他效果。根据所持对象（中间对象）和被操作对象（主要对象）的特征，可能会发生系统性结果，从而导致出现了工具的原始概念。在本文中，我们描述了使用人形机器人进行的实验，该机器人在运动场场景中探索对象与对象之间的相互作用，并学习作为两个对象的特征函数的动作影响的概率因果模型。该模型将对象的2D形状视觉提示直接链接到动作效果。由于不需要物体识别技能，因此可以通过利用形状描述符之间的相关性将其推广到新颖的物体。我们展示了在模拟环境中学习了收费模型的实验，然后在实际的机器人平台上使用了该模型，显示了效果预测中的泛化能力。我们认为，尽管在探索过程中没有为系统提供工具的概念，但是这个概念可能是从以下知识中产生的：中间对象在作用于其他对象时会产生重大影响。

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