Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.

We studied a nematic liquid crystal (LC) cylinder under the action of an axial electric field ${\mathbf{E}}_0$. Elaborate modeling of the free energy leads to the conclusion that the configuration of the molecules is “escaped radial” for low $E_0$; a phase transition, however, occurs for a critical value $E_c$, the configuration becoming axial for $E_0>E_c$. From these results, the position-dependent dielectric tensor is determined and the photonic band (PB) structure is calculated for a photonic crystal of LC cylinders. It is shown that by varying $E_0$ a PB gap can be fully tuned from open to closed. Also, switching to a supercritical field can give rise to interesting polarization and directional effects in the propagation of light.

Autonomous Vehicles (AVs) have caught people’s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial purposes, among others. The open literature contains a variety of works related to the subject. They employ a diversity of techniques ranging from probabilistic to ones based on Artificial Intelligence. The increase in computing capacity, well known to many, has opened plentiful opportunities for the algorithmic processing needed by these applications, making way for the development of autonomous navigation, in many cases with astounding results. The following paper presents a low-cost but high-performance minimal sensor open architecture implemented in a modular vehicle. It was developed in a short period of time, surpassing many of the currently available solutions found in the literature. Diverse experiments were carried out in the controlled and circumscribed environment of an autonomous circuit that demonstrates the efficiency of the applicability of the developed solution.

By observing the actions taken by operators, it is possible to determine the risk level of a work task. One method for achieving this is the recognition of human activity using biosignals and inertial measurements provided to a machine learning algorithm performing such recognition. The aim of this research is to propose a method to automatically recognize physical exertion and reduce noise as much as possible towards the automation of the Job Strain Index (JSI) assessment by using a motion capture wearable device (MindRove armband) and training a quadratic support vector machine (QSVM) model, which is responsible for predicting the exertion depending on the patterns identified. The highest accuracy of the QSVM model was 95.7%, which was achieved by filtering the data, removing outliers and offsets, and performing zero calibration; in addition, EMG signals were normalized. It was determined that, given the job strain index’s purpose, physical exertion detection is crucial to computing its intensity in future work.