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.

翻译：腿足运动的最新进展使四足操作机器人成为执行需要同时具备移动能力和操作能力（移动-操作）任务的极具前景的解决方案。在现实世界中，任务规格和/或环境约束可能要求四足操作机器人具备高冗余性以及全身运动协调能力。本研究对一套全身模型预测控制框架进行了实验评估，该框架在由37个主动关节构成的双臂四足平台上实现了实时性能。据我们所知，这是迄今采用实时全身模型预测控制进行控制所含关节数量最多的腿足操作机器人。该模型预测控制在考虑完整机器人运动学和质心动力学模型时的计算效率，依托于开源DDP变体求解器和最优控制问题公式化方法。与先前关于四足操作机器人的研究不同，该模型预测控制直接与底层关节阻抗控制器对接，无需设计瞬时全身控制器。通过CENTAURO平台执行从地面拾取重物这一挑战性任务，展示了该控制方法在实际硬件上的可行性。同时，该机器人首次实现了动态步态（小跑步态）演示。结果充分彰显了在预测控制回路中利用全身信息进行重规划的潜力。