In this paper, we propose a developmental approach that allows a robot to interpret and describe the actions of human agents by reusing previous experience. The robot first learns the association between words and object affordances by manipulating the objects in its environment. It then uses this information to learn a mapping between its own actions and those performed by a human in a shared environment. It finally fuses the information from these two models to interpret and describe human actions in light of its own experience. In our experiments, we show that the model can be used flexibly to do inference on different aspects of the scene. We can predict the effects of an action on the basis of object properties. We can revise the belief that a certain action occurred, given the observed effects of the human action. In an early action recognition fashion, we can anticipate the effects when the action has only been partially observed. By estimating the probability of words given the evidence and feeding them into a predefined grammar, we can generate relevant descriptions of the scene. We believe that this is a step toward providing robots with the fundamental skills to engage in social collaboration with humans.

中文翻译：

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超越自我：使用扎根的可供性来解释和描述他人的行为

在本文中，我们提出了一种开发方法，允许机器人通过重用以前的经验来解释和描述人类代理的行为。机器人首先通过操纵环境中的对象来学习单词和对象可供性之间的关联。然后它使用这些信息来学习它自己的动作与人类在共享环境中执行的动作之间的映射。它最终融合了这两种模型的信息，根据自身的经验来解释和描述人类的行为。在我们的实验中，我们表明该模型可以灵活地用于对场景的不同方面进行推理。我们可以根据对象属性预测动作的效果。考虑到人类行为的观察结果，我们可以修正某个行为发生的信念。在早期动作识别方式中，我们可以预测仅部分观察到动作时的效果。通过估计给定证据的单词概率并将它们输入到预定义的语法中，我们可以生成场景的相关描述。我们认为，这是向机器人提供与人类进行社会协作的基本技能的一步。