We address the problem of promoting diversity in online embodied evolution of heterogeneous robot swarms. We argue that it is not easy to adapt existing diversity algorithms from traditional evolutionary robotics to this context and describe a method in which selection is based on originality and which allows a swarm of heterogeneous agents to maintain a high degree of diversity in behavioral space. We also describe a behavioral distance measure that compares behaviors in the same conditions to provide reliable measurements in online and distributed contexts. We test the selection scheme on an open-ended survival task and show its effectiveness. Without any other pressure besides that of the environment, the evolved strategies tend toward simplicity, exploiting the existing affordances. An additional external pressure enables the emergence of rich and diverse behaviors.