This paper presents a novel approach for controlling humanoid robots pushing heavy objects using kinodynamics-based pose optimization and loco-manipulation MPC. The proposed pose optimization plans the optimal pushing pose for the robot while accounting for the unified object-robot dynamics model in steady state, robot kinematic constraints, and object parameters. The approach is combined with loco-manipulation MPC to track the optimal pose. Coordinating pushing reaction forces and ground reaction forces, the MPC allows accurate tracking in manipulation while maintaining stable locomotion. In numerical validation, the framework enables the humanoid robot to effectively push objects with a variety of parameter setups. The pose optimization generates different pushing poses for each setup and can be efficiently solved as a nonlinear programming (NLP) problem, averaging 250 ms. The proposed control scheme enables the humanoid robot to push object with a mass of up to 20 kg (118$\%$ of the robot's mass). Additionally, the MPC can recover the system when a 120 N force disturbance is applied to the object.

翻译：本文提出了一种新颖的控制方法，用于人形机器人推动重物，该方法基于动力学优化姿态与推拉操作模型预测控制（MPC）。所提出的姿态优化考虑了稳态下统一的对象-机器人动力学模型、机器人运动学约束及对象参数，规划出机器人最优推拉姿态。该方法与推拉操作MPC相结合以跟踪最优姿态。通过协调推拉力反作用力与地面反作用力，MPC在保持稳定步态的同时实现精确的操作跟踪。在数值验证中，该框架使人形机器人能够有效推动具有多种参数设置的对象。姿态优化针对每种设置生成不同推拉姿态，并可高效求解为非线性规划问题，平均耗时250毫秒。所提出的控制方案使人形机器人能够推动质量高达20千克（机器人自重的118%）的对象。此外，当对对象施加120牛的外力扰动时，MPC能够恢复系统稳定。