Kinesthetic teaching is a well-established learning by demonstration (LfD) approach
as it allows operators to intuitively generate the robot motion without additional
control devices. That is so because the operator can perform the desired motion
by grasping individual robot segments and moving them to the desired pose. The
performance of kinesthetic teaching has thus already been studied in the context of
an application requiring coarse movements, while the performance of generating fine
movements has yet to be studied. Fine movements require high positional precision, for
which teleoperation and cooperative robot tool are the two established LfD approaches.
Thus, in the first part of the thesis, we compare the performance of kinesthetic
teaching to the two approaches mentioned above. For comparison, we carefully designed
two tasks based on the required motion, with the first task requiring a precise
movement from point to point and the second task requiring a precise tracking of a
reference trajectory. In addition, to determine the suitability of each LfD approach for
fine movements, we also analyzed the influence of visual modalities on the operator’s
performance. Specifically, we developed a visual enhancement tool that allowed us
to visually zoom in on the work area under the robot’s end-effector and consequently
improve the visual detection of positioning errors during the demonstration. Thus,
operators performed demonstrations using each LfD approach with and without the
use of the visual enhancement tool.
As part of this study, a smaller parallel study which focused on the execution of
fine dynamic movements was also performed. For these movements, the dynamics of
the movement have to be appropriate in order for successful demonstration. Usually,
these movements are also generated over a relatively short distance. The findings of
this study are presented in Appendix A, as the findings are not as significant as it is
the case with other studies.
In the second part of the thesis, we analyzed the performance of different methods
which are used for demonstration generalization. Apart from an appropriate
demonstration, motion generalization is the other important issue regarding LfD. We
compared DMP and GMM, which are both established methods for movement generalization.
Additionally, we proposed a novel addition to the GMM method that improves
the generalization of fine movements without increasing the computational complexity
of the model.
The thesis concludes with a study that implements kinesthetic teaching into a
real-world environment. We used a collaborative robot to detect, collect and deposit
bacterial colonies as part of a bacterial colony identification process using mass
spectrometry. The system’s performance was evaluated based on each intermediate
procedure’s results and colony identification’s success rate. The identification results
were then compared with published data on the success rate of experienced laboratory
technicians. We have also developed a so-called teaching agent for this application
based on the findings from the first two studies. The agent consisted of two separate
functionalities. The first was an augmented reality environment that allowed the operator
to perform the demonstration more precisely using the virtual reality ovals. The
second functionality was a system that, based on the DMP method, generalized the
given demonstration and adapted it to the current process requirements.
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