The computational intelligence methods play increasingly significant role in engineering and other applied systems modeling, control and in performing decision making and optimization tasks related to them. Such intelligent computational models were created which can help efficiently solve high complexity modeling, control, and optimization tasks. There are several engineering areas and applied science, e.g. robotics, logistics where the usage of classical mathematical analytical model is precluded or it would be too complex in the computational sense. Computational intelligence algorithms and models offer a solution to this problem. The most important common features of such solutions are that they aim at acceptably suboptimal, usually approximate solutions, while keeping the computational complexity (both in the senses of space and time) at a tractable, usually low degree polynomial level. The subject of the research is on the one hand the further improvement of computational intelligence algorithms and models, and on the other hand the application of these algorithms and models in solving robotics and logistics tasks.

Recently, various types of intelligent robots have been developed for the society of the next generation. In particular, intelligent robots should continue to perform tasks in real environments such as houses, commercial facilities and public facilities. The growing need to automate daily tasks combined with new robot technologies are driving the development of human-friendly robots, i.e., safe and dependable machines, operating in the close vicinity to humans or directly interacting with persons in a wide range of domains.

The current state of technology uses classical industrial robots, which are safely kept away from humans in cages. However, in order for robots to be used in close collaboration with humans, there are major technological challenges that need to be overcome. A robot should have human-like intelligence and cognitive capabilities to co-exist with people. The concepts on adaptation, learning, and cognitive development have to be introduced in the next generation of robots. Computational intelligence techniques, such as fuzzy, neural, and evolutionary computation can help in realizing these concepts. The most important common feature of computational intelligence techniques is that they aim at acceptably suboptimal, usually approximate solutions, while keeping the computational complexity at a tractable, usually low degree polynomial level. This is an important point when we want to realize the cognitive development of robots with low cost, in terms of both a financial and an algorithmic sense.