Human–Robot Collaborative Manufacturing Cell with Learning-Based Interaction Abilities

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts.