Inferring Maps and Behaviors from Natural Language Instructions
Inferring Maps and Behaviors from Natural Language Instructions
复制标题
从自然语言指令推断地图和行为
DOI：
10.1007/978-3-319-23778-7_25
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Stentz
中科院分区：
文献类型：
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作者：
Felix Duvallet;Matthew R. Walter;T. Howard;Sachithra Hemachandra;Jean Oh;S. Teller;N. Roy;A. Stentz
自然语言为人们提供了一种灵活的，直观的方式，随着机器人与我们的房屋和工作场所中的人们一起工作，它变得越来越重要，以遵循未知环境中的指示。在说明中描述了，机器人没有直接的知识，但大多数现有的自然语言理解的方法要求机器人的环境是先验的。自然语言，没有任何对环境的知识使用这种学识渊博的分布来推断一系列与命令最一致的动作，在我们收集更多的指标时，我们可以通过模拟来评估我们的方法。遵循导航命令，其性能与完全已知的环境相当。
Natural language provides a flexible, intuitive way for people to command robots, which is becoming increasingly important as robots transition to working alongside people in our homes and workplaces. To follow instructions in unknown environments, robots will be expected to reason about parts of the environments that were described in the instruction, but that the robot has no direct knowledge about. However, most existing approaches to natural language understanding require that the robot’s environment be known a priori. This paper proposes a probabilistic framework that enables robots to follow commands given in natural language, without any prior knowledge of the environment. The novelty lies in exploiting environment information implicit in the instruction, thereby treating language as a type of sensor that is used to formulate a prior distribution over the unknown parts of the environment. The algorithm then uses this learned distribution to infer a sequence of actions that are most consistent with the command, updating our belief as we gather more metric information. We evaluate our approach through simulation as well as experiments on two mobile robots; our results demonstrate the algorithm’s ability to follow navigation commands with performance comparable to that of a fully-known environment.