This paper presents a modular manipulation pipeline for CALL-M, a mobile robot developed at CNRS-AIST JRL for semi-autonomous pick-and-place operations in convenience store environments. The system leverages a ROS 2-based architecture integrating 3D perception, grasp detection, and motion planning using MoveIt 2. The pipeline comprises modular stages - point cloud acquisition, object selection, grasp estimation, and trajectory generation - coordinated by a centralized task manager.Validation in both simulation and real-world scenarios demonstrated successful grasps. While simulation confirmed reliability under ideal conditions, real-world trials revealed challenges due to sensor noise, workspace constraints, and misalignments in grasp pose generation. Despite this, the system's modularity and adaptability make it a scalable solution for manipulation in semi-structured environments.
