The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models

The abstraction tasks are challenging for multi- modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain. Recently, inspired from Multiple Time-scale Recurrent Neural Network (MTRNN), an extension of Gated Recurrent Unit (GRU), called Multiple Time-scale Gated Recurrent Unit (MTGRU), has been proposed to learn the long-term dependencies in natural language processing. Particularly it is also able to accomplish the abstraction task for paragraphs given that the time constants are well defined. In this paper, we compare the MTRNN and MTGRU in terms of its learning performances as well as their abstraction representation on higher level (with a slower neural activation). This was done by conducting two studies based on a smaller data- set (two-dimension time sequences from non-linear functions) and a relatively large data-set (43-dimension time sequences from iCub manipulation tasks with multi-modal data). We conclude that gated recurrent mechanisms may be necessary for learning long-term dependencies in large dimension multi-modal data-sets (e.g. learning of robot manipulation), even when natural language commands was not involved. But for smaller learning tasks with simple time-sequences, generic version of recurrent models, such as MTRNN, were sufficient to accomplish the abstraction task.Comment: Accepted by IJCNN 201

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

TOWARDS THE GROUNDING OF ABSTRACT CATEGORIES IN COGNITIVE ROBOTS

The grounding of language in humanoid robots is a fundamental problem, especially in social scenarios which involve the interaction of robots with human beings. Indeed, natural language represents the most natural interface for humans to interact and exchange information about concrete entities like KNIFE, HAMMER and abstract concepts such as MAKE, USE. This research domain is very important not only for the advances that it can produce in the design of human-robot communication systems, but also for the implication that it can have on cognitive science. Abstract words are used in daily conversations among people to describe events and situations that occur in the environment. Many scholars have suggested that the distinction between concrete and abstract words is a continuum according to which all entities can be varied in their level of abstractness. The work presented herein aimed to ground abstract concepts, similarly to concrete ones, in perception and action systems. This permitted to investigate how different behavioural and cognitive capabilities can be integrated in a humanoid robot in order to bootstrap the development of higher-order skills such as the acquisition of abstract words. To this end, three neuro-robotics models were implemented. The first neuro-robotics experiment consisted in training a humanoid robot to perform a set of motor primitives (e.g. PUSH, PULL, etc.) that hierarchically combined led to the acquisition of higher-order words (e.g. ACCEPT, REJECT). The implementation of this model, based on a feed-forward artificial neural networks, permitted the assessment of the training methodology adopted for the grounding of language in humanoid robots. In the second experiment, the architecture used for carrying out the first study was reimplemented employing recurrent artificial neural networks that enabled the temporal specification of the action primitives to be executed by the robot. This permitted to increase the combinations of actions that can be taught to the robot for the generation of more complex movements. For the third experiment, a model based on recurrent neural networks that integrated multi-modal inputs (i.e. language, vision and proprioception) was implemented for the grounding of abstract action words (e.g. USE, MAKE). Abstract representations of actions ("one-hot" encoding) used in the other two experiments, were replaced with the joints values recorded from the iCub robot sensors. Experimental results showed that motor primitives have different activation patterns according to the action's sequence in which they are embedded. Furthermore, the performed simulations suggested that the acquisition of concepts related to abstract action words requires the reactivation of similar internal representations activated during the acquisition of the basic concepts, directly grounded in perceptual and sensorimotor knowledge, contained in the hierarchical structure of the words used to ground the abstract action words.This study was financed by the EU project RobotDoC (235065) from the Seventh Framework Programme (FP7), Marie Curie Actions Initial Training Network

An investigation of fast and slow mapping

Children learn words astonishingly skilfully. Even infants can reliably “fast map” novel category labels to their referents without feedback or supervision (Carey & Bartlett, 1978; Houston-Price, Plunkett, & Harris, 2005). Using both empirical and neural network modelling methods this thesis presents an examination of both the fast and slow mapping phases of children's early word learning in the context of object and action categorisation. A series of empirical experiments investigates the relationship between within-category perceptual variability on two-year-old children’s ability to learn labels for novel categories of objects and actions. Results demonstrate that variability profoundly affects both noun and verb learning. A review paper situates empirical word learning research in the context of recent advances in the application of computational models to developmental research. Data from the noun experiments are then simulated using a Dynamic Neural Field (DNF) model (see Spencer & Schöner, 2009), suggesting that children’s early object categories can emerge dynamically from simple label-referent associations strengthened over time. Novel predictions generated by the model are replicated empirically, providing proofof- concept for the use of DNF models in simulations of word learning, as well emphasising the strong featural basis of early categorisation. The noun data are further explored using a connectionist architecture (Morse, de Greef, Belpaeme & Cangelosi, 2010) in a robotic system, providing the groundwork for future research in cognitive robotics. The implications of these different approaches to cognitive modelling are discussed, situating the current work firmly in the dynamic systems tradition whilst emphasising the value of interdisciplinary research in motivating novel research paradigms

Investigations into controllers for adaptive autonomous agents based on artificial neural networks.

This thesis reports the development and study of novel architectures for the simulation of adaptive behaviour based on artificial neural networks. There are two distinct levels of enquiry. At the primary level, the initial aim was to design and implement a unified architecture integrating sensorimotor learning and overall control. This was intended to overcome shortcomings of typical behaviour-based approaches in reactive control settings. It was achieved in two stages. Initially, feedforward neural networks were used at the sensorimotor level of a modular architecture and overall control was provided by an algorithm. The algorithm was then replaced by a recurrent neural network. For training, a form of reinforcement learning was used. This posed an intriguing composite of the well-known action selection and credit assignment problems. The solution was demonstrated in two sets of simulation studies involving variants of each architecture. These studies also showed: firstly that the expected advantages over the standard behaviour-based approach were realised, and secondly that the new integrated architecture preserved these advantages, with the added value of a unified control approach. The secondary level of enquiry addressed the more foundational question of whether the choice of processing mechanism is critical if the simulation of adaptive behaviour is to progress much beyond the reactive stage in more than a trivial sense. It proceeded by way of a critique of the standard behaviourbased approach to make a positive assessment of the potential for recurrent neural networks to fill such a role. The findings were used to inform further investigations at the primary level of enquiry. These were based on a framework for the simulation of delayed response learning using supervised learning techniques. A further new architecture, based on a second-order recurrent neural network, was designed for this set of studies. It was then compared with existing architectures. Some interesting results are presented to indicate the appropriateness of the design and the potential of the approach, though limitations in the long run are not discounted

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed