Active Robot Learning for Temporal Task Models
Active Robot Learning for Temporal Task Models
复制标题
时间任务模型的主动机器人学习
DOI：
10.1145/3171221.3171241
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
V. Kyrki
中科院分区：
文献类型：
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作者：
M. Racca;V. Kyrki
为了让机器人在部署后学习新技能，我们提出了一个主动学习框架，用于建模用户对任务执行的偏好。所提出的方法通过询问以自然语言表达的问题来交互式地收集信息。我们研究的有效性和所提出的方法和它的两个变种相比，被动学习策略的学习性能。我们进一步调查的人与机器人的互动性质的框架进行可用性研究与18个科目。结果表明，主动策略适用于非专家用户的时间任务的学习偏好。此外，研究结果为主动学习机器人的交互设计提供了见解。
With the goal of having robots learn new skills after deployment, we propose an active learning framework for modelling user preferences about task execution. The proposed approach interactively gathers information by asking questions expressed in natural language. We study the validity and the learning performance of the proposed approach and two of its variants compared to a passive learning strategy. We further investigate the human-robot-interaction nature of the framework conducting a usability study with 18 subjects. The results show that active strategies are applicable for learning preferences in temporal tasks from non-expert users. Furthermore, the results provide insights in the interaction design of active learning robots.