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

A Control Architecture with Online Predictive Planning for Position and Torque Controlled Walking of Humanoid Robots