Learning environmental knowledge from task-based human-robot dialog
Learning environmental knowledge from task-based human-robot dialog
复制标题
从基于任务的人机对话中学习环境知识
DOI：
10.1109/icra.2013.6631186
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Veloso
中科院分区：
文献类型：
--
作者：
T. Kollar;Vittorio Perera;D. Nardi;M. Veloso
提出了一种从基于任务的人机对话中学习环境知识的方法。以前的对话方法使用领域知识来限制人们可能使用的语言类型。相比之下，通过在语音上引入联合概率模型，得到的语义解析和从解析的每个元素到建筑物中的物理实体（例如，接地），我们的方法对未经训练的人与机器人交互的方式是灵活的，对语音到文本错误是鲁棒的，并且能够学习针对地图中的物理位置的引用表达（例如，以创建语义图）。我们的方法已经通过让未经训练的人与服务机器人进行交互进行了评估。从一个空的语义图开始，我们的方法能够比基线方法少问50%的问题，从而实现更有效和直观的人机对话。
This paper presents an approach for learning environmental knowledge from task-based human-robot dialog. Previous approaches to dialog use domain knowledge to constrain the types of language people are likely to use. In contrast, by introducing a joint probabilistic model over speech, the resulting semantic parse and the mapping from each element of the parse to a physical entity in the building (e.g., grounding), our approach is flexible to the ways that untrained people interact with robots, is robust to speech to text errors and is able to learn referring expressions for physical locations in a map (e.g., to create a semantic map). Our approach has been evaluated by having untrained people interact with a service robot. Starting with an empty semantic map, our approach is able ask 50% fewer questions than a baseline approach, thereby enabling more effective and intuitive human robot dialog.