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Toward a Natural Language Interface for Transferring Grasping Skills to Robots

机译:走向自然语言界面,将掌握的技能转移到机器人

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In this paper, we report on the findings of a human--robot interaction study that aims at developing a communication language for transferring grasping skills from a nontechnical user to a robot. Participants with different backgrounds and education levels were asked to command a five-degree-of-freedom human-scale robot arm to grasp five small everyday objects. They were allowed to use either commands from an existing command set or develop their own equivalent natural language instructions. The study revealed several important findings. First, individual participants were more inclined to use simple, familiar commands than more powerful ones. In most cases, once a set of instructions was found to accomplish the grasping task, few participants deviated from that set. In addition, we also found that the participant''s background does appear to play a role during the interaction process. Overall, participants with less technical backgrounds require more time and more commands on average to complete a grasping task as compared to participants with more technical backgrounds.
机译:在本文中,我们报告了一项人机交互研究的发现,该研究旨在开发一种交流语言,以将掌握的技能从非技术用户转移到机器人。要求具有不同背景和受教育程度的参与者指挥五自由度的人类规模的机器人手臂,以抓紧五个日常小物件。他们被允许使用现有命令集中的命令或开发自己的等效自然语言指令。该研究揭示了几个重要发现。首先,与更强大的命令相比,单个参与者更倾向于使用简单,熟悉的命令。在大多数情况下,一旦发现一组指令可以完成抓取任务,则很少有参与者偏离该组指令。此外,我们还发现,参与者的背景在互动过程中确实发挥了作用。总体而言,与具有更多技术背景的参与者相比,具有较少技术背景的参与者平均需要更多时间和更多命令来完成抓取任务。

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