Robotic assembly in manufacturing settings are a special type of long horizon Task and Motion Plan ning (TAMP) problem. While divising a motion plan for the robot is itself a challenging, identifying a task and learning it adds to problem’s complexity. This paper proposes a Hierarchical Learning (HL) based approach which pivots the multi-level structure to seamlessly integrate task identifica tion and sequencing with robot motion planning. Given the final assembly goal the higher-level agent emphasizes comprehending tasks and learning task plans. It generates sequences of sub-tasks, while the lower-level agent concentrates on executing the current sub-task. The higher-level agent employs a goal driven reinforcement learning (RL) approach to master the sequencing task, allowing it to adapt to unseen assemblies. Meanwhile, the lower level adopts a Learning from Demonstration (LfD) approach for motion planning, which can learn primitive skills from one-time demonstration and intel ligently combine the primitive skill for complicated tasks. The critical contribution of this work lies in the development of a novel method capable of comprehending and executing long horizon goal-driven assembly tasks without relying on expert demonstrations or explicit description of the whole assem bly. The proposed approach is validated through simulation and physical setup.