SPARC: Supervised Progressively Autonomous Robot Competencies
SPARC: Supervised Progressively Autonomous Robot Competencies
复制标题
SPARC：有监督的渐进式自主机器人能力
DOI：
10.1007/978-3-319-25554-5_60
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Tony Belpaeme
中科院分区：
文献类型：
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作者：
Emmanuel Senft;Paul E. Baxter;James Kennedy;Tony Belpaeme
绿野仙踪机器人控制方法被广泛使用，并且通常将很大的工作负担和注意力放在人类监督者身上，以确保适当的机器人行为，这可能会分散所从事任务的其他方面。我们建议，通过使机器人能够从监督者的指导下在线学习，逐渐变得更加自主，可以减少这种负载：监督渐进式自主机器人竞赛（Supervised Progressively Autonomous Robot Competition，简称PRACE）。将这一概念应用于自闭症谱系障碍儿童的机器人辅助治疗（RAT）领域，采用一种新的方法来评估学习机器人对人类主管工作量的影响。一项用户研究表明，控制学习机器人使主管能够实现与非学习机器人类似的任务性能，但干预较少，工作量感知降低。这些结果证明了WoZ概念的实用性及其在减少人类WoZ管理员负载方面的潜在有效性。
The Wizard-of-Oz robot control methodology is widely used and typically places a high burden of effort and attention on the human supervisor to ensure appropriate robot behaviour, which may distract from other aspects of the task engaged in. We propose that this load can be reduced by enabling the robot to learn online from the guidance of the supervisor to become progressively more autonomous: Supervised Progressively Autonomous Robot Competencies (SPARC). Applying this concept to the domain of Robot Assisted Therapy (RAT) for children with Autistic Spectrum Disorder, a novel methodology is employed to assess the effect of a learning robot on the workload of the human supervisor. A user study shows that controlling a learning robot enables supervisors to achieve similar task performance as with a non-learning robot, but with both fewer interventions and a reduced perception of workload. These results demonstrate the utility of the SPARC concept and its potential effectiveness to reduce load on human WoZ supervisors.