Towards Robot Learning from Spoken Language
Towards Robot Learning from Spoken Language
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DOI：
10.1145/3568294.3580053
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
K. Kodur;Manizheh Zand;Maria Kyrarini
中科院分区：
文献类型：
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作者：
K. Kodur;Manizheh Zand;Maria Kyrarini
本文提出了一个机器人学习框架，使机器人能够自动生成一系列的行动，从非结构化的口语。机器人学习框架能够区分指令和不相关的对话。研究人员从25名参与者那里收集了数据，他们被要求指示机器人在被打断和分心的情况下执行协作烹饪任务。该系统能够识别烹饪任务的指令动作序列，准确率为92.85 ± 3.87%。
The paper proposes a robot learning framework that empowers a robot to automatically generate a sequence of actions from unstructured spoken language. The robot learning framework was able to distinguish between instructions and unrelated conversations. Data were collected from 25 participants, who were asked to instruct the robot to perform a collaborative cooking task while being interrupted and distracted. The system was able to identify the sequence of instructed actions for a cooking task with an accuracy of of 92.85 ± 3.87%.