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首页> 外文会议>International symposium on experimental robotics;ISER'11; 20080713-16;20080713-16; Athens(GR);Athens(GR) >Teaching Robots via Natural Nonverbal Cues
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Teaching Robots via Natural Nonverbal Cues

机译：通过自然的非语言提示教学机器人

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As personal robots enter the social environments of our workplaces and homes, it will be important for them to be able to learn from a wide demographic of people. Our research seeks to identify simple, natural, and prevalent human teaching cues that are useful for directing the attention of robot learners so that robots can learn efficiently and effectively from these interactions. This research goal is significant for several reasons. First, most people do not have expertise in robotics or machine learning techniques. Second, personal robots will have to learn new tasks and skills within the bounds of human attention and patience. Third, people bring a lifetime of experience in learning from and teaching others and naturally do quite a lot to socially structure appropriate learning environments and interactions so that others can learn efficiently and effectively. Personal robots should be able to leverage these social interactions to also learn efficiently and effectively from people. The hypothesis that we investigate in this paper concerns the structure of social behavior and embodied interaction that we term "social filters:" dynamic, embodied cues through which the teacher can guide the behavior of the robot by emphasizing and de-emphasizing objects in the environment. Specifically, we conjecture that action timing and "spatial scaffolding" are two important kinds of social filters - simple, natural, and prevalent non-verbal cues in which teachers time their actions and use their bodies to structure the learning environment to direct the attention of the learner. The challenge is to identify when and how these cues are employed in natural learning settings, and how they can be used by a robot to efficiently guide its internal attention and learning processes.
机译：随着个人机器人进入我们工作场所和家庭的社交环境，对他们来说，能够从广泛的人群中学习是很重要的。我们的研究旨在确定简单，自然和普遍的人类教学线索，这些线索对引导机器人学习者的注意力很有用，以便机器人可以从这些交互中高效地学习。该研究目标之所以有意义，有几个原因。首先，大多数人没有机器人技术或机器学习技术的专业知识。其次，个人机器人将必须在人类关注和耐心的范围内学习新的任务和技能。第三，人们带来了一生的向他人学习和教学的经验，自然会在社会上构建适当的学习环境和互动方面做很多工作，以便他人可以高效地学习。个人机器人应该能够利用这些社交互动来有效地向人们学习。我们在本文中研究的假设涉及社会行为的结构和我们称为“社会过滤器”的体现的交互：动态的，体现的提示，通过这些提示，教师可以通过强调和不强调环境中的对象来指导机器人的行为。 。具体来说，我们推测动作时机和“空间脚手架”是两种重要的社会过滤器-简单，自然和普遍的非语言提示，在这些提示中，老师计时自己的动作并利用身体构造学习环境，以引导学习者的注意力。学习者。挑战在于确定在自然学习环境中何时以及如何使用这些线索，以及机器人如何使用它们来有效地引导其内部注意力和学习过程。

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