“Hello, I’m the TERMINATOR, and I’ll be your server today”. Diners might soon be feeling this greeting, with Optimus Prime in the kitchen and Wall-E then sending your order to C-3PO. In our daily lives, a version of that future is already showing up. Robotics companies are designing robots to handle tasks, including serving, interacting, collaborating, and helping. These service robots are intended to coexist with humans and engage in relationships that lead them to a better quality of life in our society. Their constant evolution and the arising of new challenges lead to an update of the existing systems. This update provides a generic vision of two questions: the advance of service robots, and more importantly, how these robots are applied in society (professional and personal) based on the market application. In this update, a new category is proposed: catering robotics. This proposal is based on the technological advances that generate new multidisciplinary application fields and challenges. Waiter robots is an example of the catering robotics. These robotic platforms might have social capacities to interact with the consumer and other robots, and at the same time, might have physical skills to perform complex tasks in professional environments such as restaurants. This paper explains the guidelines to develop a waiter robot, considering aspects such as architecture, interaction, planning, and execution.

New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid.

La logística es un sector que está en continuo crecimiento, debido tanto a la globalización, como a la actual situación creada por el Covid. En este artículo se describe una aplicación para reconocer cajas, extrayendo sus características con el fin de identificar la cara de apertura por medio de un sistema de visión por computador. Este objetivo se ha conseguido teniendo en cuenta las dimensiones y la posición en el espacio de la misma, logrando estas características a través de técnicas de procesamiento de imagen en 2D y en 3D. Posteriormente, la información correspondiente a las caras de la caja es clasificada con un árbol de decisiones, obteniendo así la probabilidad de que cada una de las seis caras sea la de apertura. Este artículo sirve para establecer las bases para desarrollar en un futuro una aplicación en la que el robot humanoide TEO mediante aprendizaje encuentre la forma más óptima de bimanipular cajas y abrirlas, integrando este conocimiento en un sistema automatizado.

Humanoid robots often struggle with tasks such as lifting objects due to the complexity involved in identifying contact points, applying the correct force, and tracking task progress. We propose an integrated solution that leverages the dual-arm capability of humanoids and utilizes sensor fusion from vision and force sensors. Our system employs a computer vision algorithm to detect and characterize object properties (shape, size, position, orientation) and differentiate between parallel and non-parallel bi-manipulation tasks. The controller then identifies optimal contact points for the end effectors, generating trajectories fed into a closed-loop controller using force feedback. For parallel bi-manipulation, momentum cancellation is achieved through sensor fusion. For non-parallel surfaces, a reinforcement learning algorithm determines the appropriate lifting force to prevent slippage using only two contact points. Experimental validation on a real humanoid platform demonstrates the effectiveness of our approach in autonomously lifting objects, regardless of contact surface configuration. This advancement significantly enhances the reliability and versatility of humanoid robots in performing complex manipulation tasks, contributing to their practical deployment in human-oriented environments.

Automating the action of finding the opening side of a box is not possible if the robot is not capable of reaching and evaluating all of its sides. To achieve this goal, in this paper, three different movement strategies to bi-manipulate a box are studied: overturning, lifting, and spinning it over a surface. First of all, the dynamics involved in each of the three movement strategies are studied using physics equations. Then, a set of experiments are conducted to determine if the real response of the humanoid robot, TEO, to a box is consistent with the expected answer based on theoretical calculus. After the dynamics validation, the information on the forces and the position in the end effectors is used to characterize these movements and create its primitives. These primitive movements will be used in the future to design a hybrid position–force control in order to adapt the movements to different kinds of boxes. The structure of this control is also presented in this paper.