Artificial neural networks can model cortical local learning and signal processing, but they are not the brain, neither are many special purpose systems to which they contribute. Autonomous mental development models all or part of the brain (or the central nervous system) and how it develops and learns autonomously from infancy to adulthood. Like neural network research, such modeling aims to be biologically plausible. This paper discusses why autonomous development is necessary according to a concept called task muddiness. Then it introduces recent results for a series of research issues, including the new paradigm for autonomous development, mental architectures, developmental algorithm, a refined classification of types of machine learning, spatial complexity and time complexity. Finally, the paper presents some experimental results for applications, including: visionguided navigation, object finding, object-based attention (eye-pan), and attention-guided pre-reaching, four tasks which infants learn to perform early but very perceptually challenging for robots. Key words: cognitive development, autonomous learning, mental architecture, on-line learning, incremental learning, visual learning, working memory, long-term memory, self-organization, regression, autonomous navigation, attention selection, object recognition,

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: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) 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.

Abstract—There exists a large conceptual gap between symbolic models and emergent models for the mind. Many emergent models work on low-level sensory data, while many symbolic models deal with high-level abstract (i.e., action) symbols. There has been relatively little study on intermediate representations, mainly because of a lack of knowledge about how representations fully autonomously emerge inside the closed brain skull, using information from the exposed two ends (the sensory end and the motor end). As reviewed here, this situation is changing. A fundamental challenge for emergent modelsisabstraction,which symbolic models enjoy through human handcrafting. The term abstract refers to properties disassociated with any particular form. Emergent abstraction seems possible, although the brain appears to never receive a computer symbol (e.g., ASCII code) or produce such a symbol. This paper reviews major agent models with an emphasis on representation. It suggests two different ways to relate symbolic representations with emergent representations: One is based on their categorical definitions. The other considers that a symbolic representation corresponds to a brain’s outside behaviors observed and handcrafted by other outside human observers; but an emergent representation is inside the brain. Index Terms—Agents, attention, brain architecture, complexity, computer vision, emergent representation, graphic models, mental

Abstract—It is elusive how the skull-enclosed brain enables spatio–temporal multimodal developmental learning. By multimodal, we mean that the system has at least two sensory modalities, e.g., visual and auditory in our experiments. By spatio–temporal, we mean that the behavior from the system depends not only on the spatial pattern in the current sensory inputs, but also those of the recent past. Traditional machine learning requires humans to train every module using hand-transcribed data, using handcrafted symbols among modules, and hand-link modules internally. Such a system is limited by a static set of symbols and static module performance. A key characteristic of developmental learning is that the “brain ” is “skull-closed ” after birth—not directly manipulatable by the system designer—so that the system can continue to learn incrementally without the need for reprogramming. In this paper, we propose an architecture for multimodal developmental learning—parallel modality pathways all situate between a sensory end and the motor end. Motor signals are not only used as output behaviors, but also as part of input to all the related pathways. For example, the proposed developmental learning does not use silence as cut points for speech processing or motion static points as key frames for visual processing. Index Terms—Developmental architecture, multimodal development, speech recognition, visual recognition.

Abstract—There has been no prior general purpose brainmind model for multiple events in complex backgrounds. I first discuss that although the age of brain-mind seems to have arrived, the current infrastructure does not well fit the need of research and peer-review for the challenging and important subject of brain-mind. Then, I present a general purpose model of the brain-mind, called the Epigenetic Developer (ED) network model. The model proposes five necessary “chunks ” for the brain picture: development, architecture, area, space and time. The development chunk means that any practical brain, natural or artificial, needs to autonomously develop through interactions with the natural environments without any previously given set of tasks. The architecture chunk handles (1) multiple objects in complex backgrounds; (2) reasoning under abstract contexts; (3) multiple sensory modalities and multiple motor modalities and their integration. The area chunk addresses the issue of feature development and area representation, without rigidly specifying what each neuron does. The space chunk deals with spatial attention to individual objects in complex backgrounds, to satisfy the invariance and specificity criteria for type, location, and other concepts. The time chunk indicates that the brain uses its intrinsic spatial mechanisms to deals with time, without dedicated temporal components. The model copes with temporal contexts of events, to satisfy invariance and specificity criteria for time warping, time duration, temporal attention, and long temporal length. The theory and mechanisms are presented and some related experimental results are summarized. I.

Abstract. An introduction is provided to artificial agent methodologies applicable to control engineering of autonomous vehicles and robots. The fundamentals that make a machine autonomous are considered: decision making that involves cognitive modelling the environment and forming data abstractions for symbolic processing and logic based reasoning. Capabilities such as navigation, path planning, tracking control and communications are treated as basic skills of cognitive agents. The ANSI standard of intelligent systems is used followed by the fundamental types of possible agent architectures for autonomous vehicles are presented, starting from reactive, through layered, to advanced architectures in terms of beliefs, goals and intentions. Cognitive capabilities of agents can fill in missing links between computer science results on discrete agents and engineering results of continuous world sensing, actuation and path planning. Design tools for “abstractions programming ” are identified as needed to fill in the gap between logic based reasoning and sensing. Contents 1.