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首页> 外文会议>IEEE International Conference on Robotics and Automation >Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation
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Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation

机译：帮助机器人学习：通过虚拟现实远程操作演示的人机交互机器人学徒模型

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摘要

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human’s time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot’s skill and to generalize its capabilities, especially as learning improves.
机译：随着人工智能成为增强机器人能力的越来越普遍的方法，重要的是考虑培训这些学习管道并利用人类专业知识的有效方法。努力实现这些目标，提出了一个主学徒模型，并在掌握任务中进行了评估，以获得有效性和​​人类感知。学徒模型通过演示，有效地使用人类的时间和专业知识来增强自我监督的学习，同时利用人类的时间和专业知识，同时促进未来对多个机器人的监督的可扩展性;当机器人无法自主完成任务时，人类通过虚拟现实提供示范。实验结果表明，机器人比单独自我监督方法和较少的人类干预措施更快地学习掌握任务，比仅仅是基于示范的方法;在150克拉有19个示范之后获得100％的掌握成功。初步用户研究评估系统的工作量，可用性和有效性，产生了系统可扩展性和部署性的有希望的结果。他们还建议用户倾向于高估机器人的技能并概括其能力，特别是作为学习改善。

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