Robot motion planning is a field that encompasses many different problems and algorithms. From the traditional piano mover's problem to more complicated kinodynamic planning problems, motion planning requires a broad breadth of human expertise and time to design well functioning algorithms. A traditional motion planning pipeline consists of modeling a system and then designing a planner and planning heuristics. Each part of this pipeline can incorporate machine learning. Planners and planning heuristics can benefit from machine learned heuristics, while system modeling can benefit from model learning. Each aspect of the motion planning pipeline comes with trade offs between computational effort and human effort. This work explores algorithms that allow motion planning algorithms and frameworks to find a compromise between the two. First, a framework for learning heuristics for sampling-based planners is presented. The efficacy of the framework depends on human designed features and policy architecture. Next, a framework for learning system models is presented that incorporates human knowledge as constraints. The amount of human effort can be modulated by the quality of the constraints given. Lastly, semi-automatic constraint generation is explored to enable a larger range of trade-offs between human expert constraint generation and data driven constraint generation. We apply these techniques and show results in a variety of robotic systems.