Anthony F. Morse

Epigenetic Robotics Architecture (ERA)

In this paper, we discuss the requirements of cognitive architectures for epigenetic robotics, and highlight the wider role that they can play in the development of the cognitive sciences. We discuss the ambitious goals of ongoing development, scalability, concept use and transparency, and introduce the epigenetic robotics architecture (ERA) as a framework guiding modeling efforts. A formal implementation is provided, demonstrated, and discussed in terms of meeting these goals. Extensions of the architecture are also introduced and we show how the dynamics of resulting models can transparently account for a wide range of psychological phenomena, without task dependant tuning, thereby making progress in all of the goal areas we highlight.

Posture Affects How Robots and Infants Map Words to Objects

For infants, the first problem in learning a word is to map the word to its referent; a second problem is to remember that mapping when the word and/or referent are again encountered. Recent infant studies suggest that spatial location plays a key role in how infants solve both problems. Here we provide a new theoretical model and new empirical evidence on how the body – and its momentary posture – may be central to these processes. The present study uses a name-object mapping task in which names are either encountered in the absence of their target (experiments 1–3, 6 & 7), or when their target is present but in a location previously associated with a foil (experiments 4, 5, 8 & 9). A humanoid robot model (experiments 1–5) is used to instantiate and test the hypothesis that body-centric spatial location, and thus the bodies’ momentary posture, is used to centrally bind the multimodal features of heard names and visual objects. The robot model is shown to replicate existing infant data and then to generate novel predictions, which are tested in new infant studies (experiments 6–9). Despite spatial location being task-irrelevant in this second set of experiments, infants use body-centric spatial contingency over temporal contingency to map the name to object. Both infants and the robot remember the name-object mapping even in new spatial locations. However, the robot model shows how this memory can emerge –not from separating bodily information from the word-object mapping as proposed in previous models of the role of space in word-object mapping – but through the body’s momentary disposition in space.

Word and category learning in a continuous semantic domain: comparing cross-situational and interactive learning

The problem of how young learners acquire the meaning of words is fundamental to language development and cognition. A host of computational models exist which demonstrate various mechanisms in which words and their meanings can be transferred between a teacher and learner. However these models often assume that the learner can easily distinguish between the referents of words, and do not show if the learning mechanisms still function when there is perceptual ambiguity about the referent of a word. This paper presents two models that acquire meaning-word mappings in a continuous semantic space. The first model is a cross-situational learning model in which the learner induces word-meaning mappings through statistical learning from repeated exposures. The second model is a social model, in which the learner and teacher engage in a dyadic learning interaction to transfer word-meaning mappings. We show how cross-situational learning, despite there being no information to the learner as to the exact referent of a word during learning, still can learn successfully. However, social learning outperforms cross-situational strategies both in speed of acquisition and performance. The results suggest that cross-situational learning is efficient for situations where referential ambiguity is limited, but in more complex situations social learning is the more optimal strategy.

Aquila: An open-source GPU-accelerated toolkit for cognitive and neuro-robotics research

This paper presents a novel open-source software application, Aquila, developed as a part of the ITALK and RobotDoC projects. The software provides many different tools and biologically-inspired models, useful for cognitive and developmental robotics research. Aquila addresses the need for high-performance robot control by adopting the latest parallel processing paradigm, based on the NVidia CUDA technology. The software philosophy, implementation, functionalities and performance are described together with three practical examples of selected modules.

The power of words

Language is special, yet its power to facilitate communication may have distracted researchers from the power of another, potential precursor ability: the ability to label things, and the effect this can have in transforming or extending cognitive abilities. In this paper we present a simple robotic model, using the iCub robot, demonstrating the effects of spatial grouping, binding, and linguistic tagging in extending our cognitive abilities.

The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about ones own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each others development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agents capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.

Towards a Bio-inspired Cognitive Architecture for Short-Term Memory in Humanoid Robots

Short-Term Memory (STM) is a crucial weapon in humans’ cognitive arsenal as it allows them to avoid a purely sensory-motor existence. Many theoretical models have been proposed over the years attempting to capture the essential mechanisms of STM. Amongst these, one of the most prominent is the “Multi-Store Model” proposed by Atkinson and Shiffrin [1], later extended into the “Search of Associative Memory” model [2]. Replicating STM capabilities on a robotic platform is not a challenging issue if we just look at it as a mechanism to store information for the time needed to perform a certain task. Rather, more interesting is to investigate how to provide a robot with such feature in a simple and biologically-inspired fashion. The literature does not abound of examples on the topic, with the few exceptions of the researches of Gallagher [3], Wang [4], and Alan [5], whose models do not seem to account in a plausible way for the mechanisms we see at work in human beings.

Social Development of Artificial Cognition

Recent years have seen a growing interest in applying insights from developmental psychology to build artificial intelligence and robotic systems. This endeavour, called developmental robotics, not only is a novel method of creating artificially intelligent systems, but also offers a new perspective on the development of human cognition. While once cognition was thought to be the product of the embodied brain, we now know that natural and artificial cognition results from the interplay between an adaptive brain, a growing body, the physical environment and a responsive social environment. This chapter gives three examples of how humanoid robots are used to unveil aspects of development, and how we can use development and learning to build better robots. We focus on the domains of word-meaning acquisition, abstract concept acquisition and number acquisition, and show that cognition needs embodiment and a social environment to develop. In addition, we argue that Spiking Neural Networks offer great potential for the implementation of artificial cognition on robots.

Embodied language learning and cognitive bootstrapping: methods and design principles

Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results. Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel co-development of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods.

Snapshots of Sensorimotor Perception: Putting the Body Back into Embodiment

Sensorimotor theories of perception are highly appealing to A.I. due to their apparent simplicity and power; however, they are not problem free either. This paper will presents a frank appraisal of sensorimotor perception discussing and highlighting the good, the bad, and the ugly with respect to a potential sensorimotor A.I.