A general concept for the gaze control of a redundant humanoid robot head is presented. It is based on an adaptive Kalman filter that predicts the next state of the moving target, processing the position information provided by a head-mounted stereo camera. The trajectory tracking control at the task level combines a proportional feedback and a feedforward term. The gains of both control actions are adapted in order to provide optimal dynamic response for unknown arbitrary target trajectories. Inverse differential kinematics is evaluated so that human-like joint motions are achieved. To exploit kinematic redundancy, a weighted pseudoinverse is realized that takes into account different optimization criteria. Additional self-motions of the head are also considered. Experimental results on the head of the humanoid robot ARMAR-III are presented. © 2011 IEEE

Abstract-In this paper we present an approach to transfer human-like reflex behavior to robots by utilizing leaky integrate-and-fire neurons. For the acceptance of robots in general and humanoid robots, which are even closer to people's daily life, in particular a main aspect is their appearance and how they act and move in human centered environments. Especially safety strategies are crucial for a widespread acceptance of these machines. In our work we target this safety aspect by approaching this issue from the direction how humans respond to external stimuli. To achieve such human-like reflexes a general reflex unit, based on special variants of the leaky integrate-and-fire neuron model has been built. Instances of this reflex unit are adapted to special reflex types and connected to form dependent reflex behaviors. The concept of these neural structures and its evaluation by means of several experiments are presented in this paper. The results are depicted in detail and future aspects of our ongoing work are addressed.

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Within the next years a new generation of humanoid robots able to manage autonomously sophisticated tasks in a complex, time varying domestic and public environment is going to be developed. To cope with these advanced requirements a new multi-sensor based discrete-continuous supervisory control concept is proposed, which is able to accomplish even complex human skills. Each skill is divided into a sequence of elementary actions (so called Primitive Skills). Depending on the multi-sensor perception of the current state of the system, the discrete control has to provide an optimal selection and activation of the appropriate sequence of action and control strategy. An on-line decision making algorithm based on the structure of Primitive Skills (PS) has been implemented. On a lower level the continuous control has to assure that each PS is performed by means of the most appropriate sub-controllers. The theoretical approach and first experimental results of the ongoing research are presented in this paper

In this paper a new visual servoing concept for dynamic grasping for humanoid robots will be presented. It relies on a stereo camera in the robot head for wide range observing in combination with a miniaturized close range camera integrated in a five finger hand. By optimal fusion of both camera information using a fuzzy decision making algorithm a robust visually controlled grasping of objects is achieved even in the case of disturbed signals or dynamic obstacles.