Learning What To Say And What To Do: A Model For Grounding Language And Actions

Automation is becoming increasingly important in nowadays society, with robots performing a lot of repetitive tasks in industry and even entering our households in the form of vacuum cleaners and lawn mowers. When considering regular tasks outside of the controlled environments of industry, robots tend to perform poorly. In particular, in situations where robots have to interact with humans, a problem arises: how can a robot understand what the human means? While a lot of work has been made in the past towards visual perception and classification of objects, but understanding what action a verb translates into has still been an unexplored area. In solving this challenge, we would enable robots to execute commands given in natural language, and also to verbalise what actions they are performing when prompted. This work studies how a robot can learn the meaning behind the sentences humans use, how it translates into its perception and the real world, but also how to translate its actions into sentences humans understand. To achieve this we propose a novel Bidirectional machine learning model, along with a data collection module that can be used by non-technical users. The main idea behind this model is the ability to generalise to novel concepts, being able to compose new sentences and actions from what it learned previously. Humans show this ability to generalise from a young age, and it is a desirable feature for this model. By using humans natural teaching instincts to teach the robot together with this generalisation ability we hope to obtain a model that allows people everywhere to teach the robot to perform the actions we desire. We validate the model in a number of tasks, using an iCub and Pepper robots physically interacting with objects in order to complete a natural language command. We test different actions, including motor actions and emotional displays, while using both transitive and intransitive verbs in the natural language commands. The main contribution of this thesis is the development of a Bidirectional Learning Algorithm, applied to a Multiple Timescale Recurrent Neural Network enabling these models to link action and language in a bidirectional way. A second contribution sees the extension of Multiple Timescale architectures to Long Short-Term Memory models, increasing the capabilities of these models. Finally the third contribution is in the form of data collection modules, with the development of an easy-to-use module based on physical interaction and speech to provide the iCub and Pepper robots with the data to be learned

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Attentional mechanisms for socially interactive robots – a survey

This review intends to provide an overview of the state of the art in the modeling and implementation of automatic attentional mechanisms for socially interactive robots. Humans assess and exhibit intentionality by resorting to multisensory processes that are deeply rooted within low-level automatic attention-related mechanisms of the brain. For robots to engage with humans properly, they should also be equipped with similar capabilities. Joint attention, the precursor of many fundamental types of social interactions, has been an important focus of research in the past decade and a half, therefore providing the perfect backdrop for assessing the current status of state-of-the-art automatic attentional-based solutions. Consequently, we propose to review the influence of these mechanisms in the context of social interaction in cutting-edge research work on joint attention. This will be achieved by summarizing the contributions already made in these matters in robotic cognitive systems research, by identifying the main scientific issues to be addressed by these contributions and analyzing how successful they have been in this respect, and by consequently drawing conclusions that may suggest a roadmap for future successful research efforts

Bridging the gap between emotion and joint action

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd. Joint action brings individuals (and embodiment of their emotions) together, in space and in time. Yet little is known about how individual emotions propagate through embodied presence in a group, and how joint action changes individual emotion. In fact, the multi-agent component is largely missing from neuroscience-based approaches to emotion, and reversely joint action research has not found a way yet to include emotion as one of the key parameters to model socio-motor interaction. In this review, we first identify the gap and then stockpile evidence showing strong entanglement between emotion and acting together from various branches of sciences. We propose an integrative approach to bridge the gap, highlight five research avenues to do so in behavioral neuroscience and digital sciences, and address some of the key challenges in the area faced by modern societies

Natural Computing and Beyond

This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners