A Control Architecture with Online Predictive Planning for Position and Torque Controlled Walking of Humanoid Robots
This work addresses the challenge of achieving compliant and versatile walking for humanoid robots, presenting an incremental improvement by combining existing methods into a nested architecture.
The paper tackled the problem of generating walking patterns for humanoid robots by proposing a three-layer control architecture that integrates kinematic planning, predictive control, and torque control, enabling both position and torque-controlled walking with improved compliance, as demonstrated in real-world experiments on the iCub robot.
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.