The current learning pipelines for robotics manipulation infer movement primitives sequentially along the temporal-evolving axis, which can result in an accumulation of prediction errors and subsequently cause the visual observations to fall out of the training distribution. This paper proposes a novel hierarchical behavior cloning approach which tries to dissociate standard behaviour cloning (BC) pipeline to two stages. The intuition of this approach is to eliminate accumulation errors using a fixed spatial representation. At first stage, a high-level planner will be employed to translate the initial observation of the scene into task-specific spatial waypoints. Then, a low-level robotic path planner takes over the task of guiding the robot by executing a set of pre-defined elementary movements or actions known as primitives, with the goal of reaching the previously predicted waypoints. Our hierarchical keypoints-based paradigm aims to simplify existing temporalevolving approach to a more simple way: directly spatialize the whole sequential primitives as a set of 8D waypoints only from the very first observation. Plentiful experiments demonstrate that our paradigm can achieve comparable results with Reinforcement Learning (RL) and outperforms existing offline BC approaches, with only a single-shot inference from the initial observation. Code and models are available at : https: //github.com/KevinRiou22/spatial-fixing-il