๐ค AI Summary
This study systematically investigates the trade-offs between usability and task performance across three prevalent robot teaching modalities: kinesthetic guidance, joystick-based teleoperation, and gesture-based demonstration. Through a user study involving three manipulation tasks of increasing complexity, the methods are quantitatively evaluated using the NASA-TLX workload scale, task reproduction success rates, and error-type analysis. Results indicate that kinesthetic guidance significantly outperforms the alternatives in direction-sensitive and contact-intensive tasks, while joystick control achieves the highest efficiency in simple pick-and-place operations. Although gesture-based teaching exhibits lower overall reliability, it approaches the performance of kinesthetic guidance in specific scenarios. These findings delineate the operational boundaries of each teaching modality and provide empirical support for task-driven selection of robot programming interfaces.
๐ Abstract
Instructing robots from demonstrations can be done through different teaching modalities, each with different usability and performance trade-offs. This paper compares kinesthetic guidance, joystick teleoperation, and hand gestures in a user study with eight participants. We evaluate replay success, modified NASA-TLX workload, and common teaching errors across three manipulation tasks. Kinesthetic guidance produced the shortest demonstrations, lowest workload, and highest success on the more orientation-sensitive and contact-rich tasks. Joystick teleoperation performed best on simple peg picking. Hand-gesture teaching, although less reliable overall, performed better than expected and in some cases achieved results comparable to kinesthetic guidance.