This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via multi-contact Model Predictive Control (MPC) framework. The proposed framework includes a multi-contact dynamics model capable of capturing various contact modes in loco-manipulation, such as hand-object contact and foot-ground contacts. Our proposed dynamics model represents the object dynamics as an external force acting on the system, which simplifies the model and makes it feasible for solving the MPC problem. In numerical validations, our multi-contact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multi-tasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.

翻译：本文提出一种基于多接触模型预测控制(MPC)框架的新型方法，用于控制人形机器人在多接触模式下的动态移动操作。所提出的框架包含一个能够捕捉移动操作中多种接触模式（如手-物体接触与脚-地面接触）的多接触动力学模型。我们提出的动力学模型将物体动力学表示为作用于系统的外力，从而简化模型并使其适用于求解MPC问题。在数值验证中，我们的多接触MPC框架仅需每个任务的接触时序与期望状态，即可为MPC赋予对移动操作预测时域内接触模式变化的认知。该框架可控制人形机器人完成多任务动态移动操作应用，例如在转身行走过程中高效拾取与放置物体。