We used a hybrid combination of evolution and learning for automatically obtaining robot controllers. Additionally, we employed the complementary reinforcement backpropagation algorithm, which integrates either positive reinforcements or punishments with supervised connectionist learning for artificial neural network robot behavior controllers. The algorithm was adapted to consider a continuous range in the outputs of the neural network controller. Furthermore, we added Differential Evolution to integrate the advantages of run-time learning with those of evolutionary learning. We ran some tests for validating this approach to obtain robust robotic behavior controllers.

As a part of the final goal of introducing robots in the human environment, it also comes the problem of the close interaction between these two beings. Close cooperative activities illustrate well this problem and rises new challenges to solve.