RONov 22, 2020

Experimental Assessment of Human-Robot Teaming for Multi-Step Remote Manipulation with Expert Operators

arXiv:2011.10898v118 citations
AI Analysis

This research addresses the problem of improving dexterity and situational awareness for expert operators in remote robot manipulation, which is crucial for applications like robotic surgery and disaster response.

This paper investigates human-robot teaming with teleautonomy and assisted planning for multi-step remote manipulation using a dual-arm robot. The proposed approach (Condition D) achieved task times comparable to direct teleoperation (Conditions A, B) while improving re-grasps, collisions, and reducing workload metrics. Compared to a similar interface without assisted planning (Condition C), Condition D reduced task time and equalized performance across 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). 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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