  This paper presents a novel approach for controlling humanoid robots to push
heavy objects. The approach combines kinodynamics-based pose optimization and
loco-manipulation model predictive control (MPC). The proposed pose
optimization considers the object-robot dynamics model, robot kinematic
constraints, and object parameters to plan the optimal pushing pose for the
robot. The loco-manipulation MPC is used to track the optimal pose by
coordinating pushing and ground reaction forces, ensuring accurate manipulation
and stable locomotion. Numerical validation demonstrates the effectiveness of
the framework, enabling the humanoid robot to push objects with various
parameter setups. The pose optimization can be solved as a nonlinear
programming (NLP) problem within an average of 250 ms. The proposed control
scheme allows the humanoid robot to push objects weighing up to 20 kg (118$\%$
of the robot's mass). Additionally, it can recover the system from a 120 N
lateral force disturbance applied for 0.3 s.
