Constructing a Martian habitat presents significant challenges due to extreme temperature variations and a low-density and -pressure atmosphere. To address these challenges a habitat constructed from prefabricated, interlocking Voronoi-based components that are assembled by human-robot collaboration has been explored in the Rhizome projects at TU Delft. In this paper, we propose a combined robot motion planning and learning method that can optimize human involvement in assembly tasks in on-site construction. The proposed hybrid approach exploits motion planning to create motion trajectories for aspects of the task where robot autonomy is capable of solving the problem on its own using sensors and intelligence. When the task becomes too difficult for existing planning capabilities, the human can step in and teach motion trajectories via kinaesthetic demonstration using Dynamic Movement Primitives (DMPs). The trajectories are then executed on the low level by an impedance controller to handle the physical interaction with the environment during the assembly. The decision-making process is managed by a behavior tree.