In order to enable robots to interact with humans in a natural way, they need to be able to autonomously learn new tasks. The most natural way for humans to tell another agent, which can be a human or robot, to perform a task is via natural language. Thus, natural human–robot interactions also require robots to understand natural language, i.e. extract the meaning of words and phrases. To do this, words and phrases need to be linked to their corresponding percepts through grounding. Afterward, agents can learn the optimal micro-action patterns to reach the goal states of the desired tasks. Most previous studies investigated only learning of actions or grounding of words, but not both. Additionally, they often used only a small set of tasks as well as very short and unnaturally simplified utterances. In this paper, we introduce a framework that uses reinforcement learning to learn actions for several tasks and cross-situational learning to ground actions, object shapes and colors, and prepositions. The proposed framework is evaluated through a simulated interaction experiment between a human tutor and a robot. The results show that the employed framework can be used for both action learning and grounding.

中文翻译：

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模拟人机交互中的动作学习和接地

为了使机器人能够以自然的方式与人类互动，它们需要能够自主学习新任务。人类告诉另一个代理（可以是人类或机器人）执行任务的最自然方式是通过自然语言。因此，自然的人机交互也需要机器人理解自然语言，即提取单词和短语的含义。为此，单词和短语需要通过接地连接到它们相应的感知。之后，代理可以学习最佳微动作模式以达到所需任务的目标状态。以前的大多数研究只调查了行为的学习或词的基础，而不是两者兼而有之。此外，他们通常只使用一小部分任务以及非常短且不自然地简化的话语。在本文中，我们引入了一个框架，该框架使用强化学习来学习多个任务的动作，并使用跨情境学习来学习地面动作、物体形状和颜色以及介词。通过人类导师和机器人之间的模拟交互实验来评估所提出的框架。结果表明，所采用的框架可用于动作学习和接地。