The field of humanoid robotics is moving towards more advanced control strategies for locomotion and manipulation. Researchers are exploring new approaches to stabilize humanoid robots during walking and object manipulation, such as decoupling upper-body and lower-body control, optimizing step timing and double support phase duration, and using nonlinear model predictive control. These innovations have the potential to enable humanoid robots to perform delicate tasks, such as carrying fragile objects, and navigate complex environments. Notable papers in this area include ones that propose a Slow-Fast Two-Agent framework for gentle humanoid locomotion and end-effector stabilization control, and a phase-based nonlinear Model Predictive Control framework for humanoid walking stabilization with single and double support time adjustments. These papers demonstrate significant advancements in the field, with results showing improved stability and control during locomotion and manipulation tasks.