  A common approach to the generation of walking patterns for humanoid robots
consists in adopting a layered control architecture. This paper proposes an
architecture composed of three nested control loops. The outer loop exploits a
robot kinematic model to plan the footstep positions. In the mid layer, a
predictive controller generates a Center of Mass trajectory according to the
well-known table-cart model. Through a whole-body inverse kinematics algorithm,
we can define joint references for position controlled walking. The outcomes of
these two loops are then interpreted as inputs of a stack-of-task QP-based
torque controller, which represents the inner loop of the presented control
architecture. This resulting architecture allows the robot to walk also in
torque control, guaranteeing higher level of compliance. Real world experiments
have been carried on the humanoid robot iCub.
