A contribution to the incorporation of sociability and creativity skills to computers and robots

This dissertation contains the research and work completed by the PhD candidate on the incorporation of sociability and creativity skills to computers and robots. Both skills can be directly related with empathy, which is the ability to understand and share the feelings of another. In this form, this research can be contextualized in the framework of recent developments towards the achievement of empathy machines. The first challenge at hands refers to designing pioneering techniques based on the use of social robots to improve user experience interacting with them. In particular, research focus is on eliminating or minimizing pain and anxiety as well as loneliness and stress of long-term hospitalized child patients. This challenge is approached by developing a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, a multiagent learning system is introduced based on a combination of machine learning and cloud computing using low-cost robots (Innvo labs's Pleo rb). Moreover, a wireless communication system is also developed for the Pleo robot in order to help the health professional who conducts therapy with the child, monitoring, understanding, and controlling Pleo behavior at any moment. As a second challenge, a new formulation of the concept of creativity is proposed in order to empower computers with. Based on previous well established theories from Boden and Wiggins, this thesis redefines the formal mechanism of exploratory and transformational creativity in a way which facilitates the computational implementation of these mechanisms in Creativity Support Systems. The proposed formalization is applied and validated on two real cases: the first, about chocolate designing, in which a novel and flavorful combination of chocolate and fruit is generated. The second case is about the composition of a single voice tune of reel using ABC notation

A contribution to the incorporation of sociability and creativity skills to computers and robots

This dissertation contains the research and work completed by the PhD candidate on the incorporation of sociability and creativity skills to computers and robots. Both skills can be directly related with empathy, which is the ability to understand and share the feelings of another. In this form, this research can be contextualized in the framework of recent developments towards the achievement of empathy machines. The first challenge at hands refers to designing pioneering techniques based on the use of social robots to improve user experience interacting with them. In particular, research focus is on eliminating or minimizing pain and anxiety as well as loneliness and stress of long-term hospitalized child patients. This challenge is approached by developing a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, a multiagent learning system is introduced based on a combination of machine learning and cloud computing using low-cost robots (Innvo labs's Pleo rb). Moreover, a wireless communication system is also developed for the Pleo robot in order to help the health professional who conducts therapy with the child, monitoring, understanding, and controlling Pleo behavior at any moment. As a second challenge, a new formulation of the concept of creativity is proposed in order to empower computers with. Based on previous well established theories from Boden and Wiggins, this thesis redefines the formal mechanism of exploratory and transformational creativity in a way which facilitates the computational implementation of these mechanisms in Creativity Support Systems. The proposed formalization is applied and validated on two real cases: the first, about chocolate designing, in which a novel and flavorful combination of chocolate and fruit is generated. The second case is about the composition of a single voice tune of reel using ABC notation.Postprint (published version

Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots

This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them

Design Strategies for Adaptive Social Composition: Collaborative Sound Environments

In order to develop successful collaborative music systems a variety of subtle interactions need to be identified and integrated. Gesture capture, motion tracking, real-time synthesis, environmental parameters and ubiquitous technologies can each be effectively used for developing innovative approaches to instrument design, sound installations, interactive music and generative systems. Current solutions tend to prioritise one or more of these approaches, refining a particular interface technology, software design or compositional approach developed for a specific composition, performer or installation environment. Within this diverse field a group of novel controllers, described as ‘Tangible Interfaces’ have been developed. These are intended for use by novices and in many cases follow a simple model of interaction controlling synthesis parameters through simple user actions. Other approaches offer sophisticated compositional frameworks, but many of these are idiosyncratic and highly personalised. As such they are difficult to engage with and ineffective for groups of novices. The objective of this research is to develop effective design strategies for implementing collaborative sound environments using key terms and vocabulary drawn from the available literature. This is articulated by combining an empathic design process with controlled sound perception and interaction experiments. The identified design strategies have been applied to the development of a new collaborative digital instrument. A range of technical and compositional approaches was considered to define this process, which can be described as Adaptive Social Composition. Dan Livingston

Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey

Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by $autotelic$ $agents$: intrinsically motivated learning agents that can learn to represent, generate, select and solve their own problems. In recent years, the convergence of developmental approaches with deep reinforcement learning (RL) methods has been leading to the emergence of a new field: $developmental$ $reinforcement$ $learning$. Developmental RL is concerned with the use of deep RL algorithms to tackle a developmental problem -- the $intrinsically$ $motivated$ $acquisition$ $of$ $open$-$ended$ $repertoires$ $of$ $skills$. The self-generation of goals requires the learning of compact goal encodings as well as their associated goal-achievement functions. This raises new challenges compared to standard RL algorithms originally designed to tackle pre-defined sets of goals using external reward signals. The present paper introduces developmental RL and proposes a computational framework based on goal-conditioned RL to tackle the intrinsically motivated skills acquisition problem. It proceeds to present a typology of the various goal representations used in the literature, before reviewing existing methods to learn to represent and prioritize goals in autonomous systems. We finally close the paper by discussing some open challenges in the quest of intrinsically motivated skills acquisition

A Biosymtic (Biosymbiotic Robotic) Approach to Human Development and Evolution. The Echo of the Universe.

In the present work we demonstrate that the current Child-Computer Interaction paradigm is not potentiating human development to its fullest – it is associated with several physical and mental health problems and appears not to be maximizing children’s cognitive performance and cognitive development. In order to potentiate children’s physical and mental health (including cognitive performance and cognitive development) we have developed a new approach to human development and evolution. This approach proposes a particular synergy between the developing human body, computing machines and natural environments. It emphasizes that children should be encouraged to interact with challenging physical environments offering multiple possibilities for sensory stimulation and increasing physical and mental stress to the organism. We created and tested a new set of computing devices in order to operationalize our approach – Biosymtic (Biosymbiotic Robotic) devices: “Albert” and “Cratus”. In two initial studies we were able to observe that the main goal of our approach is being achieved. We observed that, interaction with the Biosymtic device “Albert”, in a natural environment, managed to trigger a different neurophysiological response (increases in sustained attention levels) and tended to optimize episodic memory performance in children, compared to interaction with a sedentary screen-based computing device, in an artificially controlled environment (indoors) - thus a promising solution to promote cognitive performance/development; and that interaction with the Biosymtic device “Cratus”, in a natural environment, instilled vigorous physical activity levels in children - thus a promising solution to promote physical and mental health

Soft Morphological Computation

Soft Robotics is a relatively new area of research, where progress in material science has powered the next generation of robots, exhibiting biological-like properties such as soft/elastic tissues, compliance, resilience and more besides. One of the issues when employing soft robotics technologies is the soft nature of the interactions arising between the robot and its environment. These interactions are complex, and the their dynamics are non-linear and hard to capture with known models. In this thesis we argue that complex soft interactions can actually be beneficial to the robot, and give rise to rich stimuli which can be used for the resolution of robot tasks. We further argue that the usefulness of these interactions depends on statistical regularities, or structure, that appear in the stimuli. To this end, robots should appropriately employ their morphology and their actions, to influence the system-environment interactions such that structure can arise in the stimuli. In this thesis we show that learning processes can be used to perform such a task. Following this rationale, this thesis proposes and supports the theory of Soft Morphological Computation (SoMComp), by which a soft robot should appropriately condition, or ‘affect’, the soft interactions to improve the quality of the physical stimuli arising from it. SoMComp is composed of four main principles, i.e.: Soft Proprioception, Soft Sensing, Soft Morphology and Soft Actuation. Each of these principles is explored in the context of haptic object recognition or object handling in soft robots. Finally, this thesis provides an overview of this research and its future directions.AHDB CP17