There are numerous publications on the use of machine learning algorithms to improve the efficiency of robotic systems in general. Recently, attention has focussed on the progress of deep learning methods, which has resulted in many efforts to utilize the corresponding tools in robotics. This monograph focuses specifically on integrating machine learning tools to improve the efficiency, convergence, and applicability of SBMPs. The publication covers a wide range of robotic applications, including, but not limited to, manipulation planning, and planning for systems with dynamic constraints. In particular, this manuscript first reviews the attempts to use machine learning to improve the performance of individual primitives used by SBMPs. It also studies a series of planners that use machine learning to adaptively select from a set of motion planning primitives. The monograph then proceeds to study a series of integrated architectures that learn an end-to-end mapping of sensor inputs to robot trajectories or controls. Finally, the monograph shows how SBMPs can operate over learned models of robotic system due to the presence of noise and uncertainty, and it concludes with a comparative discussion of the different approaches covered in terms of their impact on computational efficiency of the planner, quality of the computed paths as well as the usability of SBMPs. Also outlined are the broad difficulties and limitations of these methods, as well as potential directions of future work.