| Abstract |
In our vision, evolution serves two purposes: on the one hand to allow robots to adapt to the environment and to behave so that they can operate at all. On the other hand, evolution is a force to promote task-performance, where we interpret `task' in a broad sense: it is any user-defined preference with a measurable level of compliance. It can be a direct task, like collecting rubbish (measured by the amount of rubbish cleared), but also more indirect, like energy efficiency (measured by battery lifetime). Combining these two (seemingly) contradictory roles of evolution is a generic, fundamental challenge that to our knowledge has to date not been tackled successfully.
I will discuss our ideas and some early experimental research with our novel MONEE (Multi-Objective aNd open-Ended Evolution) algorithm that combines the open-ended and task-driven aspects of evolution. |