TeleopLab: Accessible and Intuitive Teleoperation of a Robotic Manipulator for Remote Labs
Chen, Ziling, Yoon, Yeo Jung, Bautista-Montesano, Rolando, Zhao, Zhen, Mandlekar, Ajay, Liu, John
–arXiv.org Artificial Intelligence
Teleoperation offers a promising solution for enabling hands-on learning in remote education, particularly in environments requiring interaction with real-world equipment. However, such remote experiences can be costly or non-intuitive. To address these challenges, we present TeleopLab, a mobile device teleoperation system that allows students to control a robotic arm and operate lab equipment. TeleopLab comprises a robotic arm, an adaptive gripper, cameras, lab equipment for a diverse range of applications, a user interface accessible through smartphones, and video call software. We conducted a user study, focusing on task performance, students' perspectives toward the system, usability, and workload assessment. Our results demonstrate a 46.1% reduction in task completion time as users gained familiarity with the system. Quantitative feedback highlighted improvements in students' perspectives after using the system, while NASA TLX and SUS assessments indicated a manageable workload of 38.2 and a positive usability of 73.8. TeleopLab successfully bridges the gap between physical labs and remote education, offering a scalable and effective platform for remote STEM learning.
arXiv.org Artificial Intelligence
Sep-9-2025
- Country:
- Africa > Nigeria
- Adamawa State (0.04)
- Asia > Singapore (0.04)
- North America > United States
- Massachusetts > Middlesex County > Cambridge (0.04)
- Africa > Nigeria
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- Questionnaire & Opinion Survey (1.00)
- Research Report > New Finding (1.00)
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