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，机器人社区一直在寻求使机器人能够有效地从人类教练那里学习新技能。虽然受到机器人易于教学的想法的激励，但对实现这一目标至关重要的几个方面缺乏关注。该项目将通过关注从日常人类伙伴学习的问题，推进机器人通过演示学习（LbD）的最新技术水平。LbD工作通常由专家评估，通常是系统设计师。 我们将开发一个类人社交机器人的实现和实验，以解决以下研究问题：1。在连续的体验中，机器人如何使用来自环境和人的线索来分割自己的学习样本？2.人类使用什么社会线索来表示环境的显著方面？机器人可以使用什么表达机制来向人类伙伴传达学习状态的相关方面？3.模仿学习比典型学习更广泛？人类演示机器人重复？LbD相互作用。机器人如何使用人类作为信息源，而不是概括重复运动轨迹的方法？例如，它可能会偏向于与物体进行交互吗？它是人类与之互动的见证。