Introduction: The Fourth International Workshop on Epigenetic Robotics

As in the previous editions, this workshop is trying to be a forum for multi-disciplinary research ranging from developmental psychology to neural sciences (in its widest sense) and robotics including computational studies. This is a two-fold aim of, on the one hand, understanding the brain through engineering embodied systems and, on the other hand, building artificial epigenetic systems. Epigenetic contains in its meaning the idea that we are interested in studying development through interaction with the environment. This idea entails the embodiment of the system, the situatedness in the environment, and of course a prolonged period of postnatal development when this interaction can actually take place. This is still a relatively new endeavor although the seeds of the developmental robotics community were already in the air since the nineties (Berthouze and Kuniyoshi, 1998; Metta et al., 1999; Brooks et al., 1999; Breazeal, 2000; Kozima and Zlatev, 2000). A few had the intuition – see Lungarella et al. (2003) for a comprehensive review – that, intelligence could not be possibly engineered simply by copying systems that are “ready made” but rather that the development of the system fills a major role. This integration of disciplines raises the important issue of learning on the multiple scales of developmental time, that is, how to build systems that eventually can learn in any environment rather than program them for a specific environment. On the other hand, the hope is that robotics might become a new tool for brain science similarly to what simulation and modeling have become for the study of the motor system. Our community is still pretty much evolving and “under construction” and for this reason, we tried to encourage submissions from the psychology community. Additionally, we invited four neuroscientists and no roboticists for the keynote lectures. We received a record number of submissions (more than 50), and given the overall size and duration of the workshop together with our desire to maintain a single-track format, we had to be more selective than ever in the review process (a 20% acceptance rate on full papers). This is, if not an index of quality, at least an index of the interest that gravitates around this still new discipline

Motion representation with spiking neural networks for grasping and manipulation

Die Natur bedient sich Millionen von Jahren der Evolution, um adaptive physikalische Systeme mit effizienten Steuerungsstrategien zu erzeugen. Im Gegensatz zur konventionellen Robotik plant der Mensch nicht einfach eine Bewegung und führt sie aus, sondern es gibt eine Kombination aus mehreren Regelkreisen, die zusammenarbeiten, um den Arm zu bewegen und ein Objekt mit der Hand zu greifen. Mit der Forschung an humanoiden und biologisch inspirierten Robotern werden komplexe kinematische Strukturen und komplizierte Aktor- und Sensorsysteme entwickelt. Diese Systeme sind schwierig zu steuern und zu programmieren, und die klassischen Methoden der Robotik können deren Stärken nicht immer optimal ausnutzen. Die neurowissenschaftliche Forschung hat große Fortschritte beim Verständnis der verschiedenen Gehirnregionen und ihrer entsprechenden Funktionen gemacht. Dennoch basieren die meisten Modelle auf groß angelegten Simulationen, die sich auf die Reproduktion der Konnektivität und der statistischen neuronalen Aktivität konzentrieren. Dies öffnet eine Lücke bei der Anwendung verschiedener Paradigmen, um Gehirnmechanismen und Lernprinzipien zu validieren und Funktionsmodelle zur Steuerung von Robotern zu entwickeln. Ein vielversprechendes Paradigma ist die ereignis-basierte Berechnung mit SNNs. SNNs fokussieren sich auf die biologischen Aspekte von Neuronen und replizieren deren Arbeitsweise. Sie sind für spike- basierte Kommunikation ausgelegt und ermöglichen die Erforschung von Mechanismen des Gehirns für das Lernen mittels neuronaler Plastizität. Spike-basierte Kommunikation nutzt hoch parallelisierten Hardware-Optimierungen mittels neuromorpher Chips, die einen geringen Energieverbrauch und schnelle lokale Operationen ermöglichen. In dieser Arbeit werden verschiedene SNNs zur Durchführung von Bewegungss- teuerung für Manipulations- und Greifaufgaben mit einem Roboterarm und einer anthropomorphen Hand vorgestellt. Diese basieren auf biologisch inspirierten funktionalen Modellen des menschlichen Gehirns. Ein Motor-Primitiv wird auf parametrische Weise mit einem Aktivierungsparameter und einer Abbildungsfunktion auf die Roboterkinematik übertragen. Die Topologie des SNNs spiegelt die kinematische Struktur des Roboters wider. Die Steuerung des Roboters erfolgt über das Joint Position Interface. Um komplexe Bewegungen und Verhaltensweisen modellieren zu können, werden die Primitive in verschiedenen Schichten einer Hierarchie angeordnet. Dies ermöglicht die Kombination und Parametrisierung der Primitiven und die Wiederverwendung von einfachen Primitiven für verschiedene Bewegungen. Es gibt verschiedene Aktivierungsmechanismen für den Parameter, der ein Motorprimitiv steuert — willkürliche, rhythmische und reflexartige. Außerdem bestehen verschiedene Möglichkeiten neue Motorprimitive entweder online oder offline zu lernen. Die Bewegung kann entweder als Funktion modelliert oder durch Imitation der menschlichen Ausführung gelernt werden. Die SNNs können in andere Steuerungssysteme integriert oder mit anderen SNNs kombiniert werden. Die Berechnung der inversen Kinematik oder die Validierung von Konfigurationen für die Planung ist nicht erforderlich, da der Motorprimitivraum nur durchführbare Bewegungen hat und keine ungültigen Konfigurationen enthält. Für die Evaluierung wurden folgende Szenarien betrachtet, das Zeigen auf verschiedene Ziele, das Verfolgen einer Trajektorie, das Ausführen von rhythmischen oder sich wiederholenden Bewegungen, das Ausführen von Reflexen und das Greifen von einfachen Objekten. Zusätzlich werden die Modelle des Arms und der Hand kombiniert und erweitert, um die mehrbeinige Fortbewegung als Anwendungsfall der Steuerungsarchitektur mit Motorprimitiven zu modellieren. Als Anwendungen für einen Arm (3 DoFs) wurden die Erzeugung von Zeigebewegungen und das perzeptionsgetriebene Erreichen von Zielen modelliert. Zur Erzeugung von Zeigebewegun- gen wurde ein Basisprimitiv, das auf den Mittelpunkt einer Ebene zeigt, offline mit vier Korrekturprimitiven kombiniert, die eine neue Trajektorie erzeugen. Für das wahrnehmungsgesteuerte Erreichen eines Ziels werden drei Primitive online kombiniert unter Verwendung eines Zielsignals. Als Anwendungen für eine Fünf-Finger-Hand (9 DoFs) wurden individuelle Finger-aktivierungen und Soft-Grasping mit nachgiebiger Steuerung modelliert. Die Greif- bewegungen werden mit Motor-Primitiven in einer Hierarchie modelliert, wobei die Finger-Primitive die Synergien zwischen den Gelenken und die Hand-Primitive die unterschiedlichen Affordanzen zur Koordination der Finger darstellen. Für jeden Finger werden zwei Reflexe hinzugefügt, zum Aktivieren oder Stoppen der Bewegung bei Kontakt und zum Aktivieren der nachgiebigen Steuerung. Dieser Ansatz bietet enorme Flexibilität, da Motorprimitive wiederverwendet, parametrisiert und auf unterschiedliche Weise kombiniert werden können. Neue Primitive können definiert oder gelernt werden. Ein wichtiger Aspekt dieser Arbeit ist, dass im Gegensatz zu Deep Learning und End-to-End-Lernmethoden, keine umfangreichen Datensätze benötigt werden, um neue Bewegungen zu lernen. Durch die Verwendung von Motorprimitiven kann der gleiche Modellierungsansatz für verschiedene Roboter verwendet werden, indem die Abbildung der Primitive auf die Roboterkinematik neu definiert wird. Die Experimente zeigen, dass durch Motor- primitive die Motorsteuerung für die Manipulation, das Greifen und die Lokomotion vereinfacht werden kann. SNNs für Robotikanwendungen ist immer noch ein Diskussionspunkt. Es gibt keinen State-of-the-Art-Lernalgorithmus, es gibt kein Framework ähnlich dem für Deep Learning, und die Parametrisierung von SNNs ist eine Kunst. Nichtsdestotrotz können Robotikanwendungen - wie Manipulation und Greifen - Benchmarks und realistische Szenarien liefern, um neurowissenschaftliche Modelle zu validieren. Außerdem kann die Robotik die Möglichkeiten der ereignis- basierten Berechnung mit SNNs und neuromorpher Hardware nutzen. Die physikalis- che Nachbildung eines biologischen Systems, das vollständig mit SNNs implementiert und auf echten Robotern evaluiert wurde, kann neue Erkenntnisse darüber liefern, wie der Mensch die Motorsteuerung und Sensorverarbeitung durchführt und wie diese in der Robotik angewendet werden können. Modellfreie Bewegungssteuerungen, inspiriert von den Mechanismen des menschlichen Gehirns, können die Programmierung von Robotern verbessern, indem sie die Steuerung adaptiver und flexibler machen

Autonomous Decision-Making based on Biological Adaptive Processes for Intelligent Social Robots

Mención Internacional en el título de doctorThe unceasing development of autonomous robots in many different scenarios drives a new revolution to improve our quality of life. Recent advances in human-robot interaction and machine learning extend robots to social scenarios, where these systems pretend to assist humans in diverse tasks. Thus, social robots are nowadays becoming real in many applications like education, healthcare, entertainment, or assistance. Complex environments demand that social robots present adaptive mechanisms to overcome different situations and successfully execute their tasks. Thus, considering the previous ideas, making autonomous and appropriate decisions is essential to exhibit reasonable behaviour and operate well in dynamic scenarios. Decision-making systems provide artificial agents with the capacity of making decisions about how to behave depending on input information from the environment. In the last decades, human decision-making has served researchers as an inspiration to endow robots with similar deliberation. Especially in social robotics, where people expect to interact with machines with human-like capabilities, biologically inspired decisionmaking systems have demonstrated great potential and interest. Thereby, it is expected that these systems will continue providing a solid biological background and improve the naturalness of the human-robot interaction, usability, and the acceptance of social robots in the following years. This thesis presents a decision-making system for social robots acting in healthcare, entertainment, and assistance with autonomous behaviour. The system’s goal is to provide robots with natural and fluid human-robot interaction during the realisation of their tasks. The decision-making system integrates into an already existing software architecture with different modules that manage human-robot interaction, perception, or expressiveness. Inside this architecture, the decision-making system decides which behaviour the robot has to execute after evaluating information received from different modules in the architecture. These modules provide structured data about planned activities, perceptions, and artificial biological processes that evolve with time that are the basis for natural behaviour. The natural behaviour of the robot comes from the evolution of biological variables that emulate biological processes occurring in humans. We also propose a Motivational model, a module that emulates biological processes in humans for generating an artificial physiological and psychological state that influences the robot’s decision-making. These processes emulate the natural biological rhythms of the human organism to produce biologically inspired decisions that improve the naturalness exhibited by the robot during human-robot interactions. The robot’s decisions also depend on what the robot perceives from the environment, planned events listed in the robot’s agenda, and the unique features of the user interacting with the robot. The robot’s decisions depend on many internal and external factors that influence how the robot behaves. Users are the most critical stimuli the robot perceives since they are the cornerstone of interaction. Social robots have to focus on assisting people in their daily tasks, considering that each person has different features and preferences. Thus, a robot devised for social interaction has to adapt its decisions to people that aim at interacting with it. The first step towards adapting to different users is identifying the user it interacts with. Then, it has to gather as much information as possible and personalise the interaction. The information about each user has to be actively updated if necessary since outdated information may lead the user to refuse the robot. Considering these facts, this work tackles the user adaptation in three different ways. • The robot incorporates user profiling methods to continuously gather information from the user using direct and indirect feedback methods. • The robot has a Preference Learning System that predicts and adjusts the user’s preferences to the robot’s activities during the interaction. • An Action-based Learning System grounded on Reinforcement Learning is introduced as the origin of motivated behaviour. The functionalities mentioned above define the inputs received by the decisionmaking system for adapting its behaviour. Our decision-making system has been designed for being integrated into different robotic platforms due to its flexibility and modularity. Finally, we carried out several experiments to evaluate the architecture’s functionalities during real human-robot interaction scenarios. In these experiments, we assessed: • How to endow social robots with adaptive affective mechanisms to overcome interaction limitations. • Active user profiling using face recognition and human-robot interaction. • A Preference Learning System we designed to predict and adapt the user preferences towards the robot’s entertainment activities for adapting the interaction. • A Behaviour-based Reinforcement Learning System that allows the robot to learn the effects of its actions to behave appropriately in each situation. • The biologically inspired robot behaviour using emulated biological processes and how the robot creates social bonds with each user. • The robot’s expressiveness in affect (emotion and mood) and autonomic functions such as heart rate or blinking frequency.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Richard J. Duro Fernández.- Secretaria: Concepción Alicia Monje Micharet.- Vocal: Silvia Ross

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Explainable shared control in assistive robotics

Shared control plays a pivotal role in designing assistive robots to complement human capabilities during everyday tasks. However, traditional shared control relies on users forming an accurate mental model of expected robot behaviour. Without this accurate mental image, users may encounter confusion or frustration whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. The Explainable Shared Control paradigm introduced in this thesis attempts to resolve such model misalignment by jointly considering assistance and transparency. There are two perspectives of transparency to Explainable Shared Control: the human's and the robot's. Augmented reality is presented as an integral component that addresses the human viewpoint by visually unveiling the robot's internal mechanisms. Whilst the robot perspective requires an awareness of human "intent", and so a clustering framework composed of a deep generative model is developed for human intention inference. Both transparency constructs are implemented atop a real assistive robotic wheelchair and tested with human users. An augmented reality headset is incorporated into the robotic wheelchair and different interface options are evaluated across two user studies to explore their influence on mental model accuracy. Experimental results indicate that this setup facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment. As for human intention inference, the clustering framework is applied to a dataset collected from users operating the robotic wheelchair. Findings from this experiment demonstrate that the learnt clusters are interpretable and meaningful representations of human intent. This thesis serves as a first step in the interdisciplinary area of Explainable Shared Control. The contributions to shared control, augmented reality and representation learning contained within this thesis are likely to help future research advance the proposed paradigm, and thus bolster the prevalence of assistive robots.Open Acces

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