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交接物体时抓取类型和位置的选择。

On the choice of grasp type and location when handing over an object.

机构信息

The Biorobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pisa, Italy.

ARC Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD 4001, Australia.

出版信息

Sci Robot. 2019 Feb 13;4(27). doi: 10.1126/scirobotics.aau9757.

Abstract

The human hand is capable of performing countless grasps and gestures that are the basis for social activities. However, which grasps contribute the most to the manipulation skills needed during collaborative tasks, and thus which grasps should be included in a robot companion, is still an open issue. Here, we investigated grasp choice and hand placement on objects during a handover when subsequent tasks are performed by the receiver and when in-hand and bimanual manipulation are not allowed. Our findings suggest that, in this scenario, human passers favor precision grasps during such handovers. Passers also tend to grasp the purposive part of objects and leave "handles" unobstructed to the receivers. Intuitively, this choice allows receivers to comfortably perform subsequent tasks with the objects. In practice, many factors contribute to a choice of grasp, e.g., object and task constraints. However, not all of these factors have had enough emphasis in the implementation of grasping by robots, particularly the constraints introduced by a task, which are critical to the success of a handover. Successful robotic grasping is important if robots are to help humans with tasks. We believe that the results of this work can benefit the wider robotics community, with applications ranging from industrial cooperative manipulation to household collaborative manipulation.

摘要

人手能够执行无数种抓握和手势,这些是社交活动的基础。然而,在协作任务中,哪些抓握最有助于操作技能,因此哪些抓握应该包含在机器人伙伴中,这仍然是一个悬而未决的问题。在这里,我们研究了在交接过程中抓握选择和手的位置,当后续任务由接收者执行且不允许在手内和双手操作时。我们的研究结果表明,在这种情况下,人类传递者在这种交接中更喜欢使用精密抓握。传递者也倾向于抓住物体的有目的部分,并使“把手”不被接收者阻挡。直观地说,这种选择使接收者能够舒适地用物体执行后续任务。在实践中,许多因素会影响抓握的选择,例如物体和任务的限制。然而,并非所有这些因素在机器人的抓握实现中都得到了足够的重视,特别是任务引入的限制,这些限制对交接的成功至关重要。如果机器人要帮助人类完成任务,那么成功的机器人抓握是很重要的。我们相信这项工作的结果可以使更广泛的机器人社区受益,应用范围从工业协作操作到家庭协作操作。

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