论文标题

物体切换:机器人的评论

Object Handovers: a Review for Robotics

论文作者

Ortenzi, Valerio, Cosgun, Akansel, Pardi, Tommaso, Chan, Wesley, Croft, Elizabeth, Kulic, Dana

论文摘要

本文调查了有关人类机器人对象的文献。移交是一个协作的联合行动,代理人给予者将对象赋予另一个代理人,接收器。当接收器首先接触给予者持有的对象时,物理交换开始并在给予者完全释放到接收器的对象时结束。但是,重要的认知和物理过程在物理交换之前开始,包括就交换的位置和时机启动隐式协议。从这个角度来看,我们将审查结构为上述事件界定的两个主要阶段:1)一个前阶段,以及2)物理交换。我们将分析重点放在两个演员(送礼者和接收者)上,并报告机器人送礼者(机器人到人类移交)和机器人接收器(人身到机器人移交)的艺术状况。我们报告了通常用于评估相互作用的定性和定量指标的全面列表。在将我们的评论集中在交换的认知水平(例如预测,感知,运动计划,学习)和身体水平(例如运动,抓地力,抓地力释放)上时,我们还简要讨论了安全性,社会背景和人体工程学的概念。我们比较了人类到人类移交期间所显示的行为与机器人助手的艺术状态,并确定机器人助手的主要改进领域,以达到与人类相互作用相当的绩效。最后,我们提出了一组最小的指标集,以便在方法之间进行公平的比较。

This article surveys the literature on human-robot object handovers. A handover is a collaborative joint action where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: 1) a pre-handover phase, and 2) the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, learning) and the physical level (e.g., motion, grasping, grip release) of the handover, we briefly discuss also the concepts of safety, social context, and ergonomics. We compare the behaviours displayed during human-to-human handovers to the state of the art of robotic assistants, and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.

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