Manufacturing is shifting from autonomous robots to collaborative robots (cobots) that work alongside humans. To foster effective human-robot collaboration, the robot must be aware of the operator’s needs and be able to adapt accordingly. A fundamental step in this collaboration is the robot’s ability to recognize and understand the operator’s actions online. This study aims to develop a computer vision system to recognize and classify human actions in a collaborative assembly scenario. The goal has been to create a lightweight action recognition model, by investigating various models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, combined with YOLO-based object recognition. This work presents several model architectures designed to classify human actions based on different types of input data. Specifically, the models process hand landmark data from an RGB-D camera. Four actions commonly found in manufacturing environments are classified. A custom dataset was created from scratch to support this research. From the analysis of the different approaches, the best-performing model achieved an offline action recognition accuracy of 95%, and an online accuracy exceeding 90%. Comparing different methodologies and input data for human action recognition helps to highlight their strengths and limitations, enabling the identification of the most effective strategy for recognizing actions in human-robot collaboration. This approach contributes to significant advancements in developing more intuitive and efficient collaborative environments within manufacturing settings. Data, code, and results are available at: https://github.com/grgzpp/human-action-recognition-and-anticipation.

The key challenge in expanding human-robot collaboration is enabling robots to understand and adapt to human needs, fostering seamless interaction. This research develops an open-source framework integrated with a collaborative robotic arm to recognize, predict, and anticipate human actions in an assembly scenario. Human action classification was performed by comparing various approaches, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, combined with YOLO-based object recognition. The study utilized a pneumatic cylinder as an example component for the assembly, along with the KUKA LBR iiwa 7 R800 robotic arm. The action recognition model, developed from scratch with a custom dataset, achieved 95% offline accuracy and over 90% online. The action prediction model, trained on real assembly sequences from human demonstrations, suggests the next robot action. This approach improves the flexibility and customization by allowing the assembly sequence to be learned directly observing the operator performing the task, without prior knowledge (except for the objects to detect). This advancement enhances cobots ability to recognize, predict, and anticipate human actions, improving intuitive and efficient collaboration in manufacturing.

This paper presents the kinematic characterization of a 3-Cylindrical-Prismatic-Universal (3-CPU) parallel manipulator designed for motions of pure rotation. The machine has been conceived at the Polytechnic University of Marche, and recent studies have shown that its kinematic architecture can be exploited for the realization of reconfigurable machines with different kinds of motions (pure rotational, pure translational, and planar motions among others). The 3-CPU concept has been subject to further investigations for a deeper understanding of this peculiar behavior. After a brief introduction to these concepts, the paper faces the position and the differential kinematics of the 3-CPU spherical manipulator aiming at identifying workspace boundaries and its kinematic manipulability.