| Abstract |
Evolving robot morphologies implies the need for lifetime learning so that individual robots can learn to manipulate their bodies. An individual’s morphology will obviously combine traits of all its parents; it must adapt its own controller to suit its morphology, and cannot rely on the controller of any one parent to perform well without adaptation. This paper investigates the practicability and benefits of Lamarckian evolution in this setting.
Implementing lifetime learning by means of on-line evolution, we establish an indirect encoding scheme that com- bines Compositional Pattern Producing Networks (CPPNs) and Central Pattern Generators (CPGs) as a relevant learner and controller for open-loop gait controllers. Experimental validation shows that a Lamarckian setup with CPPN-CPG provides substantial benefits. |