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Assisted Robot Teleoperation
Comparing Teleoperation Interfaces for a Nursing Mobile Manipulator: Performance, Workload and Learning Effort
Tele-nursing robots provide promising solutions for quarantine and remote patient care. While more complex robotic systems are available to be adopted for human-robot teaming in future healthcare workplaces, it is still unclear which teleoperation interfaces are the most suitable for teleoperating a wide range of nursing tasks. End-users like nursing workers are mostly unfamiliar with robot operation and thus the teleoperation interface must be easy to learn and intuitive. We conduct a user study with (N=11) current and future nursing workers, including eight nursing students and three registered nurses, to evaluate three representative designs of contemporary robot teleoperation interfaces. These interfaces are developed using a Gamepad, a hand-held stylus, and human motion mapping. Our user study compared the usability of the interfaces based on task performance, workload and learning effort. We find that for complex robotic systems, such as a mobile manipulator nursing robot, teleoperation via human motion mapping outperforms the Gamepad and stylus-style based interfaces. The motion-mapping interface significantly reduces practice time, task completion time, teleoperation errors, number of interactions required to cycle through control modes and cognitive workload, with the trade-off being non-trivial physical fatigue for the teleoperator. Our study also reveals the desirable design features for the next generation of interfaces for nursing robot teleoperation.
Physical Fatigue Analysis of Assistive Robot Teleoperation via Whole-body Motion Mapping
Robot teleoperation via motion mapping has been demonstrated to be an efficient and intuitive approach for controlling and teaching the whole-body motion coordination of humanoid robots. However, the physical fatigue in the usage of such robot teleoperation interfaces may prevent this approach to be widely used in large scale by diverse workforce populations. As a result, this paper conducts a user study to investigate the physical fatigue of teleoperators in the whole-body motion mapping teleoperation of a mobile humanoid assistive robot. Through a Vicon motion capture system, participants teleoperated the robot to perform general purpose assistive tasks that involve reaching-to-grasp, bimanual manipulation, loco-manipulation and human-robot interaction. We assess the physical fatigue based on surface electromyography (sEMG) measurement, and compare it between different tasks and muscles. Our analysis results indicate that: (1) Fatigue happens more in the tasks that involve more precise manipulation and steady posture maintenance; (2) Deltoids, Biceps and Trapezius are used more for such tasks and thus have more fatigue than others. These findings imply that automating the fatigue-causing task components may reduce the physical fatigue in motion mapping teleoperation.
Shared Autonomous Interface for Reducing Physical Effort in Robot Teleoperation via Human Motion Mapping
Motion mapping is an intuitive method of teleoperation with a low learning curve. Our previous study investigates the physical fatigue caused by teleoperating a robot to perform general-purpose assistive tasks and this fatigue affects the operator's performance. The results from that study indicate that physical fatigue happens more in the tasks which involve more precise manipulation and steady posture maintenance. In this paper, we investigate how teleoperation assistance in terms of shared autonomy can reduce the physical workload in robot teleoperation via motion mapping. Specifically, we conduct a user study to compare the muscle effort in teleoperating a mobile humanoid robot to (1) reach and grasp an individual object and (2) collect objects in a cluttered workspace with and without an autonomous grasping function that can be triggered manually by the teleoperator. We also compare the participants' task performance, subjective user experience, and change in attitude towards the usage of teleoperation assistance in the future based on their experience using the assistance function. Our results show that: (1) teleoperation assistance like autonomous grasping can effectively reduce the physical effort, task completion time and number of errors; (2) based on their experience performing the tasks with and without assistance, the teleoperators reported that they would prefer to use automated functions for future teleoperation interfaces.
T. C. Lin, A. U. Krishnan and Z. Li, "Intuitive, Efficient and Ergonomic Tele-Nursing Robot Interfaces: Design Evaluation and Evolution", ACM Transactions on Human-Robot Interaction (THRI), 2021.
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