Robotic manipulation is widely adopted in industry but typically assumes predictable, tightly structured workspaces, where classical motion-planning pipelines can work safely, reducing as much as possible the occurrence of failures when used to control robots. However, when a failure occurs e.g. missed grasps, object slippage, or unforeseen perturbations, explicit detection and handling are required, making this approach fragile in unstructured settings. In parallel, advances in hardware such as graphics processing units (GPUs) and in machine learning have made Reinforcement Learning (RL) a practical option to learn, via trial and error, policies that directly plan and control motion while exhibiting recovery behaviors.