Performance Analysis of Learning From Demonstration Approaches During a Fine Movement Generation

Abstract

Learning from demonstration (LfD) is a well-established method of movement demonstration; however, the performance of different LfD approaches during a fine movement generation is still unknown. In this study, we compare kinesthetic teaching, teleoperation, and cooperative robot tool approaches on two different tasks, where a submillimeter accuracy is required. Additionally, we analyze the influence of a visual enhancement feature on each of the approaches and the influence of a spatial scaling feature on the teleoperation approach. The participants are a well-balanced group (regarding age, gender, and expertise), with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$65 \%$</tex-math></inline-formula> having no previous experience using robots. In our study, we found that all approaches achieved a submillimeter median positioning error. However, when no additional features are used, the cooperative robot tool (CRT) approach outperforms other approaches since it consistently achieves the lowest positioning error. Besides the positioning error, the generated velocity and the participants’ feedback (via a questionnaire) also indicates that it is the most suitable approach for an accurate submillimeter movement generation. We also concludes that the visual enhancement feature and the spatial scaling feature has a significant influence on the performance of all approaches. When the two features are used, the generated positioning error drops considerably. When the visual enhancement feature is used, kinesthetic teaching performs in some cases as good as the CRT approach, while the teleoperation with the spatial scaling feature approach in some cases even outperforms the CRT approach. However, we still consider the CRT to be the best approach for fine movement generation since these features cannot be used in every possible scenario.