This paper proposes a hybrid architecture based on hierarchical planning of modular behaviour networks for generating autonomous behaviours of the office delivery robot. Behaviour networks suitable for goaloriented problems are exploited for the architecture, where a monolithic behaviour network is decomposed into several smaller behaviour modules. In order to construct and adjust sequences of the modules the planning method considers the sub-goals, the priority in each task and the user feedback. It helps a robot to quickly react in dynamic situations as well as achieve global goals efficiently. The proposed architecture is verified on both the Webot simulator and Khepera II robot in office environment with delivery tasks. Experimental results confirms that a robot can achieve goals and generate module sequences successfully even in unpredictable situations, and the proposed planning method reduces the elapsed time during tasks by 17.5%.