For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion. From the great number of interesting information presented here, I believe that this book can offer some aid in new research, as well as stimulating the interest of people for this area of study related to the umanoid robots. This chapter presents a foveated active vision planning concept for robot navigation in order to overcome drawbacks of conventional active vision when trading field of view versus measurement accuracy. This is the first approach of task-related control of foveated active vision in the context of humanoid robots as well as localization and mapping. In a typical robot navigation scenario the benefits of foveated active vision have been demonstrated: an improved localization accuracy combined with an extended visible field compared to conventional active vision. As a generic information maximization principle has been used the gaze control strategy is generalizable to other scenarios depending on the definition of the task-related information measures, thereby, allocating vision sensor resources optimally. The second part of this chapter has presented novel concepts of coordinating gaze direction and locomotion planning. Through this planning procedure, all possible trajectories and viewing angles over a specific time horizon are anticipated. This results in significantly improved performance compared to known approaches, which neglect the limitations of the sensors. It is demonstrated that a very accurate environmental model is autonomously produced even if very inaccurate robot and sensor models are used. Such a scheme is ideal for vision-based humanoid robots. The proposed approaches assume time-invariant scenarios. Path planning and gaze control methods for dynamically changing environments are not yet covered and subject to ongoing research We proposed the `Sticky Hands' game as a novel interaction between human and robot. The game was implemented by combining a robot controller process and a learning algorithm with a Balance-Keeping Control of Upright Standing in Biped Human Beings and its Application for Stability Assessment
