A novel formulation of the synthesis of motor coordination for humanoid whole-body motion is proposed in this paper, in order to ensure robust control of postural stability. It relies on the distributed model predictive control framework to coordinate, in an optimal way, several objectives. The effectiveness of this control technique to maintain postural stability of a biped against strong external disturbances is shown. Control of the horizontal dynamics of the center of mass can withstand limited perturbations. Thus postural stability criteria are specified with respect to the robot center of mass vertical and horizontal dynamics, and to the angular dynamics of its torso. Formulating the balance problem in a predictive form and distributing at different time scales significantly increases the robustness of the system to external disturbances, in terms of both tip-over and slippage risks. This original control architecture is validated through the simulation of an iCub robot performing a walking activity under unknown external actions.

Balance strategies range from continuous postural adjustments to discrete changes in contacts: their simultaneous execution is required to maintain postural stability while considering the engaged walking activity. In order to compute optimal time, duration and position of footsteps along with the center of mass trajectory of a humanoid, a novel mixed-integer model of the system is presented. The introduction of this model in a predictive control problem brings the definition of a Mixed-Integer Quadratic Program, subject to linear constraints. Simulation results demonstrate the simultaneous adaptation of the gait pattern and posture of the humanoid, in a walking activity under large disturbances, to efficiently compromise between task performance and balance. In addition, a push recovery scenario displays how, using a single balance-performance ratio, distinct behaviors of the humanoid can be specified.

This paper proposes a robust whole-body control formulation for biped balance in disturbed conditions by manipulation tasks. In order to include the effects of the interaction of the robot with its environment, required by the manipulation task in the balance control, we introduce a distributed preview control which captures both balance and manipulation behaviors and enables the regulation of the interaction impedance. The initial ZMP preview control is extended to take into account the disturbance resulting from the manipulation task and the preview control of adaptive impedances used to drive the upper-limbs. The resulting behavior is illustrated in a simple scenario. Its aptitude to dynamically extract an optimal control strategy improving tracking performances of both manipulation and balance tasks is also assessed when complex perturbations have to be compensated.