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

职业：设计和评估的方法，机器人学习的demonstrationabstractThe的目标，这个职业生涯的建议是创建一个研究和教育计划，致力于开发和评估新的算法，机器人系统，学习从演示和与人类用户的互动。该计划的研究计划是开发算法，用于从演示和与人类用户的交互中自动生成机器人控制器。本项目的主要研究问题涉及以下方面的调查、设计和实施：（1）自主机器人控制体系结构，其提供对来自用户提供的演示的任务知识获取的支持，（2）用于通过演示进行机器人学习的算法，其促进非专业用户对机器人助手的训练，（3）定量评价指标，提供客观手段，用于评估机器人演示示教背景下的人机交互性能。建议的机器人控制架构将创建复杂的任务学习的基础设施，并将提供一个新的表示多个动作选择机制。演示学习算法将使用一种新的方法来解释用户的演示，基于粒子过滤，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所学习的任务。量化评估指标不仅为拟议的互动学习方法提供客观的衡量标准，而且还可以作为更广泛的人力资源投资领域的更通用的工具。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，而无需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UNR建立一个新的机器人实验室;（2）通过研讨会和实习机会向当地高中推广;（3）在同行评审的期刊、会议记录和互联网上传播成果。

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.