Self-assembly in biology is an inspiration for engineered large-scale multi-modular systems with desirable characteristics, such as robustness, scalability, and adaptivity. Previous works have shown that simple mobile robots can be used to emulate and study self-assembly behaviors. However, many of these studies were restricted to rather static and inflexible aggregations in predefined shapes, and were limited in adaptivity compared to that observed in nature. We propose a photomorphogenesis approach for robots using our vascular morphogenesis model-a light-stimuli directed method for multi-robot self-assembly inspired by the tissue growth of trees. Robots in the role of 'leaves' collect a virtual resource that is proportional to a real, sensed environmental feature. This is then used to build a virtual underlying network that shares a common resource throughout the whole robot aggregate and determines where it grows or shrinks as a reaction to the dynamic environment. In our approach the robots use supplemental bioinspired models to collectively select a leading robot to decide who starts to self-assemble (and where), or to assemble static aggregations. The robots then use our vascular morphogenesis model to aggregate in a directed way preferring bright areas, hence resembling natural phototropism (growth towards light). Our main result is that the assembled robots are adaptive and able to react to dynamic environments by collectively and autonomously rearranging the aggregate, discarding outdated parts, and growing new ones. In representative experiments, the self-assembling robots collectively make rational decisions on where to grow. Cutting off parts of the aggregate triggers a self-organizing repair process in the robots, and the parts regrow. All these capabilities of adaptivity, collective decision-making, and self-repair in our robot self-assembly originate directly from self-organized behavior of the vascular morphogenesis model. Our approach opens up opportunities for self-assembly with reconfiguration on short time-scales with high adaptivity of dynamic forms and structures.