Abstract
Humanoid robots are a rising trend, and are about to be sold to the public on a large scale, but for this to be possible it is necessary to make them reliable, secure and functional. This implies many improvements over the prior state of the art. A domain of improvement is the full-body control of humanoid robots. The objective of this thesis is to propose a control architecture for generating a bio-inspired full-body control. The main idea is to learn from human walking to replicate these movements on a humanoid robot. The proposed control solution uses the principle of kinematics task for four objectives: (i) the relative pose of the feet, (ii) the position of the Centre de masse (CoM), (iii) the orientation of the upper-body, and (iv) the joints' limits avoidance. Stability is enhanced by modifiying the CoM position by using a stabilizer based on nonlinear regulation of the Zero Moment Point (ZMP). The resulting approach is called hybrid kinematic / dynamic control architecture. This approach has been validated experimentally on two prototypes of humanoid robots for tasks such as squat and walking.