Using dialog and human observations to dictate tasks to a learning robot assistant
Using dialog and human observations to dictate tasks to a learning robot assistant
复制标题
使用对话和人类观察向学习机器人助手指示任务
DOI：
10.1007/s11370-008-0016-5
复制
发表时间：
2008
影响因子：
2.5
通讯作者：
M. Veloso
中科院分区：
文献类型：
--
作者：
P. Rybski;J. Stolarz;Kevin Yoon;M. Veloso
机器人助手需要以自然的方式与人互动，以便被人们的日常生活所接受。我们一直在研究机器人助手，其功能包括视觉跟踪环境中的人类，识别人类进行活动的上下文，理解口语（具有固定词汇），参与口语对话以解决歧义，以及学习任务程序。在本文中，我们描述了一个机器人的任务学习算法，在该算法中，人类显式和交互式的指导一系列的步骤，机器人通过口语。训练算法将机器人对人类的感知与理解的语音数据融合，将口语映射到机器人动作，并跟随人类收集动作适用性状态信息。机器人将获取的任务表示为条件过程，并与人类进行口语对话，以填写人类可能遗漏的信息。
Robot assistants need to interact with people in a natural way in order to be accepted into people’s day-to-day lives. We have been researching robot assistants with capabilities that include visually tracking humans in the environment, identifying the context in which humans carry out their activities, understanding spoken language (with a fixed vocabulary), participating in spoken dialogs to resolve ambiguities, and learning task procedures. In this paper, we describe a robot task learning algorithm in which the human explicitly and interactively instructs a series of steps to the robot through spoken language. The training algorithm fuses the robot’s perception of the human with the understood speech data, maps the spoken language to robotic actions, and follows the human to gather the action applicability state information. The robot represents the acquired task as a conditional procedure and engages the human in a spoken-language dialog to fill in information that the human may have omitted.