Self-assembly is the aggregation of simple parts into complex patterns as frequently observed in nature. Following this inspiration, creating programmable systems of self-assembly that achieve similar complexity and robustness with robots is challenging. As role model we pick the growth of natural plants that adapts to environmental conditions and is robust to disturbances, such as changes due to dynamic environments and cut parts. We program a robot swarm to self-assemble into tree-like shapes and to efficiently adapt to the environment. Our approach is inspired by the vascular morphogenesis of plants, that is the patterned formation of vascular tissue to transport fluids and nutrients internally. The aggregated robots establish an internal network of resource sharing, allowing them to make rational decisions collectively about where to add and where to remove robots. As an effect, the growth is adaptive to an environmental feature (here, light) and robust to changes in a dynamic environment. The robot swarm is able to self-repair by regrowing lost parts. We successfully validate and benchmark our approach in a number of robot swarm experiments showing adaptivity, robustness, and self-repair.