ABSTRACT
In this chapter we have discussed various imitation learning techniques based on kinesthetic teaching where the expert (teacher) provides demonstrations of certain task while holding the robotic manipulator and guiding it through the task trajectory. The dynamic movement primitives (DMPs) presented here, has been shown to learn task in joint space and the other methods which use Gaussian mixture model, learn the task in the Cartesian space. The task trajectory is asymptotically stable in DMP models as it use an inbuilt PD controller. Parameters of DMP based model are learnt from single demonstration in an unconstrained optimization process which make them computationally efficient. SED motion encoders which are great at generating asymptotically stable trajectories have been presented in this chapter.The parameters of the SED models are learnt in a constrained optimization process. The motion learning architecture C-FuzzStaMP is also presented in this chapter. The multitask learning framework exploits additional information to enhance multitasking capability of the proposed technique. Three algorithms are presented to describe learning using different regression techniques namely GMR, LWPR and SVR.
