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

多年来，机器人界一直在寻求使机器人能够有效地从人类训练师那里学习新技能。虽然受到易于教授机器人的想法的推动，但对这一目标至关重要的几个方面缺乏关注。这个项目将通过关注从日常人类伙伴那里学习的问题来推进机器人通过演示学习(LBD)的最新技术。LBD工作通常由专业人员进行评估，通常是系统设计人员。我们将开发一个人形社交机器人的实现和实验来解决以下研究问题：1.在连续的体验体验中，机器人如何使用来自环境和人的线索来分割自己的学习实例？2.人类使用什么社交线索来指示环境的显著方面？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.模仿学习比典型的模仿学习更广泛？人类演示机器人重复？LBD交互。除了概括重复运动轨迹的方法外，机器人如何才能将人类作为信息源？例如，与S看到的人类互动的对象互动可能是有偏见的。

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.