Development of a Semi-Autonomous Manipulation Pipeline for Robotic Shelf-Picking Operations

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 cen- tralized 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 mis- alignments in grasp pose generation. Despite this, the system’s modularity and adaptability make it a scalable solution for manipulation in semi-structured environments.