A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Development of Cognitive Capabilities in Humanoid Robots

Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

A review of theories and methods in the science of face-to-face social interaction

For most of human history, face-to-face interactions have been the primary and most fundamental way to build social relationships, and even in the digital era they remain the basis of our closest bonds. These interactions are built on the dynamic integration and coordination of verbal and non-verbal information between multiple people. However, the psychological processes underlying face-to-face interaction remain difficult to study. In this Review, we discuss three ways the multimodal phenomena underlying face-to-face social interaction can be organized to provide a solid basis for theory development. Next, we review three types of theory of social interaction: theories that focus on the social meaning of actions, theories that explain actions in terms of simple behaviour rules and theories that rely on rich cognitive models of the internal states of others. Finally, we address how different methods can be used to distinguish between theories, showcasing new approaches and outlining important directions for future research. Advances in how face-to-face social interaction can be studied, combined with a renewed focus on cognitive theories, could lead to a renaissance in social interaction research and advance scientific understanding of face-to-face interaction and its underlying cognitive foundations

Influencing robot learning through design and social interactions: a framework for balancing designer effort with active and explicit interactions

This thesis examines a balance between designer effort required in biasing a robot’s learn-ing of a task, and the effort required from an experienced agent in influencing the learning using social interactions, and the effect of this balance on learning performance. In order to characterise this balance, a two dimensional design space is identified, where the dimensions represent the effort from the designer, who abstracts the robot’s raw sensorimotor data accord-ing to the salient parts of the task to increasing degrees, and the effort from the experienced agent, who interacts with the learner robot using increasing degrees of complexities to actively accentuate the salient parts of the task and explicitly communicate about them. While the in-fluence from the designer must be imposed at design time, the influence from the experienced agent can be tailored during the social interactions because this agent is situated in the environ-ment while the robot is learning. The design space is proposed as a general characterisation of robotic systems that learn from social interactions. The usefulness of the design space is shown firstly by organising the related work into the space, secondly by providing empirical investigations of the effect of the various influences o