Using optimization to generate motion planning has been examined since the 1980s. But since optimization techniques are rather local and time consuming, they have been long surpassed by probabilistic planning approaches that are rather global. Geometric planning methods have solved a wide variety of robotic problems and have even been extended to planning by taking into account bounds on first and second derivatives of the motion (kinodynamics). Yet, their application to under-actuated and high degrees of freedom hyper-redundant robots (such as humanoids) achieving various tasks under hard constraints (perceptual tasks, motion state and torque limits, equilibrium, collision avoidance, etc.) with whole body dynamic motion is not straightforward. Optimization techniques are mostly local, but advances in mathematics of optimization generated a considerable collection of know-how with on-the-shelf robust solvers that can be efficiently used in robotic motion planning. In this talk, I will focus in planning multi-contact non-gaited motions and also on our experience using optimization techniques to plan motions for the humanoid robot HRP-2 in performing extreme tasks that are not possible to obtain with state-of-the-art planning techniques. I will also discuss challenges that remain to be handled under an optimization framework, among which is the critical issue of speeding the calculation process. I will also address what breakthroughs would result in robotics if real-time optimization can be doable.