CAREER: Design and Evaluation of Methods for Robot Learning by Demonstration

职业：设计和评估演示的机器人学习方法摘要这份职业提案的目标是创建一个研究和教育计划，致力于开发和评估机器人系统的新算法，这些算法从演示和与人类用户的交互中学习。该项目的研究计划是开发自动生成机器人控制器的算法，通过演示和与人类用户的交互来实现。本项目的主要研究问题涉及：(1)支持从用户提供的演示中获取任务知识的自主机器人控制体系结构；(2)便于非专业用户培训机器人助手的演示学习算法；(3)定量评估度量标准，为通过演示教学机器人的人机交互性能提供客观评估手段。提出的机器人控制体系结构将为复杂任务学习创建基础设施，并将为多个动作选择机制提供新的表示。演示算法的学习将使用一种新的方法来解释用户的演示，该方法基于粒子过滤，可以识别多个并发活动的叠加。此外，泛化算法将使用归纳学习方法来捕获和表示任务执行策略的变化。用户反馈将允许通过口头指示或远程操作干预来改进学习的任务。量化评价指标不仅将为拟议的互动学习方法提供客观衡量标准，而且还可以作为更广泛的人力资源倡议领域的通用工具。这项研究将为机器人在日常任务中的使用开辟新的可能性，允许人类用户根据自己的需求定制机器人，而不需要接受计算机科学家或机器人工程师的培训。该项目的教育计划有三个主要组成部分：(1)推进和促进与教学有关的活动，包括开发新的机器人课程和在联合国大学建立一个新的机器人实验室；(2)通过研讨会和实习接触当地高中；(3)在同行评议的期刊、会议记录和互联网上传播结果。

CAREER: Design and Evaluation of Methods for Robot Learning by DemonstrationAbstractThe goal of this career proposal is to create a research and educational program dedicated to developing and evaluating novel algorithms for robotic systems that learn from demonstration and interaction with human users. This program's research plan is to develop algorithms for automated generation of robot controllers from demonstration and interaction with human users. The main research questions of this project pertain to the investigation, design, and implementation of: (1) an autonomous robot control architecture that provides support for task knowledge acquisition from user provided demonstration, (2) algorithms for robot learning by demonstration that facilitate training of robot assistants by non-specialist users, (3) quantitative evaluation metrics that provide objective means for assessing the performance of human-robot interaction in the context of robot teaching by demonstration. The proposed robot control architecture will create the infrastructure for complex task learning and will provide a new representation for multiple action selection mechanisms. The learning by demonstration algorithms will use a novel approach for interpreting a user's demonstration, based on particle filtering that identifies superpositions of multiple concurrent activities. In addition, generalization algorithms will use inductive learning methods to capture and represent variations in task execution strategies. User feedback will allow for refinement of learned tasks, through verbal instructions or teleoperation interventions. The quantitative evaluation metrics will not only provide objective measures for the proposed interactive learning approach, but could also serve as more general tools for the broader field of HRI. This research will open new possibilities for the use of robots in everyday tasks, by allowing human users to customize robots to their own needs, without the necessity of being trained as computer scientists or robotics engineers. The educational plan of this project has three main components: (1) advancing and promoting teaching related activities, including the development of new robotics courses and establishing a new Robotics Laboratory at UNR, (2) outreach to local high-schools through seminars and internships and (3) dissemination of results in peer reviewed journals, conference proceedings, and on the Internet.