论文标题
镜子中的机器人：迈向镜子自我识别的具体计算模型
Robot in the mirror: toward an embodied computational model of mirror self-recognition
论文作者
论文摘要
自我识别或自我意识通常仅归因于人类和其他几个物种。这些概念的定义各不相同，对它们背后的机制知之甚少。但是，有一个像图灵测试的基准：镜子自我识别，其中包括秘密地在经过测试的对象的表面上贴上标记，将她放在镜子前，并观察反应。在这项工作中，首先，我们提供了通过该测试所需的组件的机械分解或过程模型。基于这些，我们为实证研究提供了建议。特别是，在我们看来，应详细研究婴儿或动物达到标记的方式。其次，我们开发了一个模型，以使类人形机器人NAO通过测试。我们技术贡献的核心是通过学习深层自动编码器学习面部的生成模型并利用预测误差来学习外观表示和视觉新颖性检测。该标记被确定为面部的显着区域，并触发了动作，这是依靠以前学习的映射到臂关节角度的。该体系结构在两个机器人上进行了测试，这些机器人的面部完全不同。
Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species. The definitions of these concepts vary and little is known about the mechanisms behind them. However, there is a Turing test-like benchmark: the mirror self-recognition, which consists in covertly putting a mark on the face of the tested subject, placing her in front of a mirror, and observing the reactions. In this work, first, we provide a mechanistic decomposition, or process model, of what components are required to pass this test. Based on these, we provide suggestions for empirical research. In particular, in our view, the way the infants or animals reach for the mark should be studied in detail. Second, we develop a model to enable the humanoid robot Nao to pass the test. The core of our technical contribution is learning the appearance representation and visual novelty detection by means of learning the generative model of the face with deep auto-encoders and exploiting the prediction error. The mark is identified as a salient region on the face and reaching action is triggered, relying on a previously learned mapping to arm joint angles. The architecture is tested on two robots with a completely different face.
