Understanding Teacher Gaze Patterns for Robot Learning
Understanding Teacher Gaze Patterns for Robot Learning
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DOI：
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发表时间：
2019-07
影响因子：
3.2
通讯作者：
Akanksha Saran;Elaine Schaertl Short;A. Thomaz;S. Niekum
中科院分区：
文献类型：
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作者：
Akanksha Saran;Elaine Schaertl Short;A. Thomaz;S. Niekum
已知人类注视是操纵任务期间潜在人类意图和目标的强有力指示器。这项工作研究了人类教师向机器人演示任务的凝视模式，并提出了这些模式可用于增强机器人学习的方法。使用动觉教学和视频演示，我们确定新的意图揭示凝视行为在教学过程中。这些被证明是翔实的各种问题，从参考框架推理分割的多步任务。基于我们的研究结果，我们提出了两个概念验证算法，表明凝视数据可以将多步任务的子任务分类提高6%，并将单步任务的奖励推理和策略学习提高67%。我们的研究结果提供了一个自然的人类凝视模型在机器人学习的基础，从示范设置和利用人类的凝视，以提高机器人学习目前的开放性问题。
Human gaze is known to be a strong indicator of underlying human intentions and goals during manipulation tasks. This work studies gaze patterns of human teachers demonstrating tasks to robots and proposes ways in which such patterns can be used to enhance robot learning. Using both kinesthetic teaching and video demonstrations, we identify novel intention-revealing gaze behaviors during teaching. These prove to be informative in a variety of problems ranging from reference frame inference to segmentation of multi-step tasks. Based on our findings, we propose two proof-of-concept algorithms which show that gaze data can enhance subtask classification for a multi-step task up to 6% and reward inference and policy learning for a single-step task up to 67%. Our findings provide a foundation for a model of natural human gaze in robot learning from demonstration settings and present open problems for utilizing human gaze to enhance robot learning.