Experimental Assessment of Human-Robot Teaming for Multi-Step Remote Manipulation with Expert Operators
Remote robot manipulation with human control enables applications where safety and environmental constraints are adverse to humans (e.g. underwater, space robotics and disaster response) or the complexity of the task demands human-level cognition and dexterity (e.g. robotic surgery and manufacturing...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
21.11.2020
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Subjects | |
Online Access | Get full text |
DOI | 10.48550/arxiv.2011.10898 |
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Summary: | Remote robot manipulation with human control enables applications where
safety and environmental constraints are adverse to humans (e.g. underwater,
space robotics and disaster response) or the complexity of the task demands
human-level cognition and dexterity (e.g. robotic surgery and manufacturing).
These systems typically use direct teleoperation at the motion level, and are
usually limited to low-DOF arms and 2D perception. Improving dexterity and
situational awareness demands new interaction and planning workflows. We
explore the use of human-robot teaming through teleautonomy with assisted
planning for remote control of a dual-arm dexterous robot for multi-step
manipulation tasks, and conduct a within-subjects experimental assessment (n=12
expert users) to compare it with other methods, resulting in the following four
conditions: (A) Direct teleoperation with imitation controller + 2D perception,
(B) Condition A + 3D perception, (C) Teleautonomy interface teleoperation + 2D
& 3D perception, (D) Condition C + assisted planning. The results indicate that
this approach (D) achieves task times comparable with direct teleoperation
(A,B) while improving a number of other objective and subjective metrics,
including re-grasps, collisions, and TLX workload metrics. When compared to a
similar interface but removing the assisted planning (C), D reduces the task
time and removes a significant interaction with the level of expertise of the
operator, resulting in a performance equalizer across users. |
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DOI: | 10.48550/arxiv.2011.10898 |