  Recent progress in legged locomotion has rendered quadruped manipulators a
promising solution for performing tasks that require both mobility and
manipulation (loco-manipulation). In the real world, task specifications and/or
environment constraints may require the quadruped manipulator to be equipped
with high redundancy as well as whole-body motion coordination capabilities.
This work presents an experimental evaluation of a whole-body Model Predictive
Control (MPC) framework achieving real-time performance on a dual-arm quadruped
platform consisting of 37 actuated joints. To the best of our knowledge this is
the legged manipulator with the highest number of joints to be controlled with
real-time whole-body MPC so far. The computational efficiency of the MPC while
considering the full robot kinematics and the centroidal dynamics model builds
upon an open-source DDP-variant solver and a state-of-the-art optimal control
problem formulation. Differently from previous works on quadruped manipulators,
the MPC is directly interfaced with the low-level joint impedance controllers
without the need of designing an instantaneous whole-body controller. The
feasibility on the real hardware is showcased using the CENTAURO platform for
the challenging task of picking a heavy object from the ground. Dynamic
stepping (trotting) is also showcased for first time with this robot. The
results highlight the potential of replanning with whole-body information in a
predictive control loop.
