2015
DOI: 10.1177/0018720814565188
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Analyzing the Effects of Human-Aware Motion Planning on Close-Proximity Human–Robot Collaboration

Abstract: Objective:The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort.Background:The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human–robot interaction.Metho… Show more

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Cited by 208 publications
(148 citation statements)
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References 33 publications
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“…A higher number of trials utilizing the same movement strategy could have resulted in more substantiated evaluations. As noted in [16], longer-term studies are necessary to determine whether the effects found in this study are weakened or strengthened by longer exposure to the robot and how trust and expectations develop over time. A long-term study will appear as part of follow up work.…”
Section: Longer-term Studiesmentioning
confidence: 88%
See 2 more Smart Citations
“…A higher number of trials utilizing the same movement strategy could have resulted in more substantiated evaluations. As noted in [16], longer-term studies are necessary to determine whether the effects found in this study are weakened or strengthened by longer exposure to the robot and how trust and expectations develop over time. A long-term study will appear as part of follow up work.…”
Section: Longer-term Studiesmentioning
confidence: 88%
“…Movement cues have the potential to give human coworkers insights into robot intention, which can improve human performance in terms of time to completion (TTC) [16] and improve trust [1]. To achieve this, the motion needs to be predictable.…”
Section: Movement Cuesmentioning
confidence: 99%
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“…We evaluate RAPTOR using three activity datasets: a publicly available single person activity dataset with Kinectderived skeletal position features (UTKinect [11]); a motioncapture dataset of reaching behaviors from a stationary manufacturing task (Static-Reach [7]); and a new motion capture dataset of a mobile automotive final assembly manufacturing task (Dynamic-AutoFA) depicted in Figure 2.…”
Section: Evaluation and Resultsmentioning
confidence: 99%
“…In human-robot collaboration, the ability of a team member to quickly and reliably interpret teammates' actions and intentions is critical to achieving satisfactory robot performance and team fluency. This type of anticipatory information can improve task completion time, idle time, concurrent motion, and human-robot separation distance during human-robot collaboration [1], [2], [3], [4], [5], [6], [7]. Thus, rapid classification is a key attribute of an effective approach to activity recognition, as it allows for real-time situational awareness at the speed of the available sensing hardware.…”
Section: Introductionmentioning
confidence: 99%