Anthony F. Morse's research while affiliated with University of Plymouth and other places

Learning words from ambiguous naming events is difficult. In such situations, children struggle with not attending to task irrelevant information when learning object names. The current study reduces the problem space of learning names for object categories by holding color constant between the target and other extraneous objects. We examine how this influences two types of word learning (retention and generalization) in both 30-month-old children (Experiment 1) and the iCub humanoid robot (Experiment 2). Overall, all children and iCub performed well on the retention trials, but they were only able to generalize the novel names to new exemplars of the target categories if the objects were originally encountered in sets with objects of the same colors, not if the objects were originally encountered in sets with objects of different colors. These data demonstrate that less information presented during the learning phase narrows the problem space and leads to better word learning success for both children and iCub. Findings are discussed in terms of cognitive load and desirable difficulties.

This paper presents a robot architecture heavily inspired by neuropsychology, developmental psychology and research into "executive functions" (EF) which are responsible for the planning capabilities in humans. This architecture is presented in light of this inspiration, mapping the modules to the different functions in the brain. We emphasize the importance and effects of these modules in the robot, and their similarity to the effects in humans with lesions on the frontal lobe. Developmental studies related to these functions are also considered, focusing on how they relate to the robot's different modules and how the developmental stages in a child relate to improvements in the different modules in this system. An experiment with the iCub robot is compared with experiments with humans, strengthening this similarity. Furthermore we propose an extension to this system by integrating with "Epigenetics Robotic Architecture" (ERA), a system designed to mimic how children learn the names and properties of objects. In the previous implementation of this architecture, the robot had to be taught the names of all the necessary objects before plan execution, a learning step that was entirely driven by the human interacting with the robot. With this extension, we aim to make the learning process fully robot-driven, where an iCub robot will interact with the objects while trying to recognise them, and ask a human for input if and when it does not know the objects' names.

It is well-established that toddlers can correctly select a novel referent from an ambiguous array in response to a novel label. There is also a growing consensus that robust word learning requires repeated label-object encounters. However, the effect of the context in which a novel object is encountered is less wellunderstood. We present two embodied neural network replications of recent empirical tasks, which demonstrated that the context in which a target object is encountered is fundamental to referent selection and word learning. Our model offers an explicit account of the bottom-up associative and embodied mechanisms which could support children's early word learning and emphasises the importance of viewing behaviour as the interaction of learning at multiple timescales.

Word learning is central to language acquisition. From as early as 18 months, toddlers can disambiguate the referent of a novel label from an ambiguous array, and reinforce this label-object association over repeated encounters. While cross-situational word learning has been demonstrated repeatedly, the cognitive mechanisms which underlie early word learning are less well-understood. To explore these processes we replicated two recent studies of early word learning using the iCub robot (Metta at al., 2010) and the Epigenetic Robotics Architecture (Morse, de Greef, Belpaeme & Cangelosi, 2011), instantiating word learning as a process of learning simple associations between visual and lexical input in an embodied system. Simulations captured the empirical results, and demonstrated that the context in which a to-be-learned object is encountered – both visual and temporal – is critical to referent selection and word learning. Our model offers an explicit account of the bottom-up associative and embodied mechanisms which support children’s word learning and emphasises the importance of viewing behaviour as the interaction of learning at multiple timescales. In particular, this work highlights the importance of the micro-level dynamics that emerge from the interaction between the movement of the body and the in-task context, making the prediction for future empirical work that the physical environment in which early learning events occur may have important consequences for language acquisition.

Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to “switch” between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture (Morse et al 2010) embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills.

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.

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.

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.

Language and number learning in developmental robots. Developmental Robotics is the interdisciplinar4 approach to the autonomous design of behavioural and cognitive capabilities in artiwcial agents that takes direct inspiration from the developmental principles and mechanisms observed in natural cognitive s4stems. This approach puts strong emphasis on constraining the robot's cognitive architecture and behavioural and learning performance onto kno=n child ps4cholog4 theories and data, allo=ing the modelling of the developmental succession of qualitative and quantitative stages leading to the acquisition of adult-like cognitive skills. In this paper =e present a set of studies based on the developmental robotics approach looking speciwcall4 at the modelling of embodied phenomena in the acquisition of linguistic and numerical cognition capabilities.