TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of approximately 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Intelligent Haptic Perception for Physical Robot Interaction

Doctorado en Ingeniería mecatrónica. Fecha de entrega de la Tesis doctoral: 8 de enero de 2020. Fecha de lectura de Tesis doctoral: 30 de marzo 2020.The dream of having robots living among us is coming true thanks to the recent advances in Artificial Intelligence (AI). The gap that still exists between that dream and reality will be filled by scientific research, but manifold challenges are yet to be addressed. Handling the complexity and uncertainty of real-world scenarios is still the major challenge in robotics nowadays. In this respect, novel AI methods are giving the robots the capability to learn from experience and therefore to cope with real-life situations. Moreover, we live in a physical world in which physical interactions are both vital and natural. Thus, those robots that are being developed to live among humans must perform tasks that require physical interactions. Haptic perception, conceived as the idea of feeling and processing tactile and kinesthetic sensations, is essential for making this physical interaction possible. This research is inspired by the dream of having robots among us, and therefore, addresses the challenge of developing robots with haptic perception capabilities that can operate in real-world scenarios. This PhD thesis tackles the problems related to physical robot interaction by employing machine learning techniques. Three AI solutions are proposed for different physical robot interaction challenges: i) Grasping and manipulation of humans’ limbs; ii) Tactile object recognition; iii) Control of Variable-Stiffness-Link (VSL) manipulators. The ideas behind this research work have potential robotic applications such as search and rescue, healthcare or rehabilitation. This dissertation consists of a compendium of publications comprising as the main body a compilation of previously published scientific articles. The baseline of this research is composed of a total of five papers published in prestigious peer-reviewed scientific journals and international robotics conferences

Survey of Visual and Force/Tactile Control of Robots for Physical Interaction in Spain

Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors

Visuo-Haptic Grasping of Unknown Objects through Exploration and Learning on Humanoid Robots

Die vorliegende Arbeit befasst sich mit dem Greifen unbekannter Objekte durch humanoide Roboter. Dazu werden visuelle Informationen mit haptischer Exploration kombiniert, um Greifhypothesen zu erzeugen. Basierend auf simulierten Trainingsdaten wird außerdem eine Greifmetrik gelernt, welche die Erfolgswahrscheinlichkeit der Greifhypothesen bewertet und die mit der größten geschätzten Erfolgswahrscheinlichkeit auswählt. Diese wird verwendet, um Objekte mit Hilfe einer reaktiven Kontrollstrategie zu greifen. Die zwei Kernbeiträge der Arbeit sind zum einen die haptische Exploration von unbekannten Objekten und zum anderen das Greifen von unbekannten Objekten mit Hilfe einer neuartigen datengetriebenen Greifmetrik

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science