RI-Small: An HRI Approach to Robot Learning by Demonstration

RI-Small：通过演示进行机器人学习的 HRI 方法

• 批准号: 0812106
• 负责人: Andrea Thomaz
• 金额: --
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812106&HistoricalAwards=false
• 关键词: RI; Small; HRI; Approach; Robot

For years, the robotics community has sought to enable robots to efficiently learn new skills from a human trainer. While motivated by the idea of robots that are easy to teach, there's been a lack of focus on several aspects essential to this goal. This project will advance the state of the art in robot Learning by Demonstration (LbD) by focusing the issues of learning from everyday human partners. LbD work has usually been evaluated with expert humans, typically the system designer. We will develop implementations and experiments on a humanoid social robot to address the following research questions: 1. In continuous embodied experience, how can a robot use cues from the environment and the person to segment its own learning examples? 2. What social cues does the human use to indicate salient aspects of the environment? What expressive mechanisms can the robot use to communicate relevant aspects of the learning state to the human partner? 3. Imitation learning is broader than the typical ?human-demo robot-repeat? LbD interaction. How can the robot use the human as an information source beyond generalizing a way to repeat motion trajectories? For example, it may be biased to interact with objects it?s seen the human interact with.

多年来，机器人社区一直试图使机器人能够从人类教练中有效学习新技能。尽管受到易于教学的机器人的想法的动机，但一直缺乏关注该目标必不可少的几个方面。该项目将通过专注于每天的人类合作伙伴的学习问题来通过演示（LBD）来推动机器人学习的现状。通常，LBD工作已与专家人类（通常是系统设计师）进行评估。 我们将在人类机器人机器人上开发实施和实验，以解决以下研究问题：1。在连续体现的经验中，机器人如何使用环境中的线索和人来细分自己的学习示例？ 2.人类用来指示环境的显着方面的社会提示？机器人可以使用哪些表达性机制将学习状态的相关方面传达给人类伴侣？ 3。模仿学习比典型的？ LBD相互作用。机器人如何将人类用作信息源，而不是概括重复运动轨迹的方法？例如，它与对象相互作用可能是偏见的。