Datensatz

Zusammenfassung

While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application
has often been limited by the complexities of manually obtaining
sufficiently accurate models. In the past, learning has proven a viable alternative
to using a combination of rigid-body dynamics and handcrafted
approximations of nonlinearities. However, a major open question is what
nonparametric learning method is suited best for learning dynamics? Traditionally,
locally weighted projection regression (LWPR), has been the
standard method as it is capable of online, real-time learning for very complex
robots. However, while LWPR has had significant impact on learning
in robotics, alternative nonparametric regression methods such as support
vector regression (SVR) and Gaussian processes regression (GPR) offer interesting alternatives with fewer open parameters and potentially higher
accuracy. In this paper, we evaluate these three alternatives for model
learning. Our comparison consists out of the evaluation of learning quality
for each regression method using original data from SARCOS robot
arm, as well as the robot tracking performance employing learned models.
The results show that GPR and SVR achieve a superior learning precision
and can be applied for real-time control obtaining higher accuracy. However,
for the online learning LWPR presents the better method due to its
lower computational requirements.