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

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”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: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) 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.Peer reviewe

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Uma comparação entre arquiteturas cognitivas : análise teórica e prática

Orientador: Ricardo Ribeiro GudwinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta uma comparação teórica e prática entre três das mais populares arquiteturas cognitivas: SOAR, CLARION e LIDA. A comparação teórica é realizada com base em um conjunto de funções cognitivas supostamente existentes no ciclo cognitivo humano. A comparação prática é realizada aplicando-se um mesmo experimento em todas as arquiteturas, coletando alguns dados e comparando-as usando como base algumas métricas de qualidade de software. O objetivo é enfatizar semelhanças e diferenças entre os modelos e implementações, com o objetivo de aconselhar um novo usuário a escolher a arquitetura mais apropriada para uma certa aplicaçãoAbstract: This work presents a theoretical and practical comparison of three popular cognitive architectures: SOAR, CLARION, and LIDA. The theoretical comparison is performed based on a set of cognitive functions supposed to exist in the human cognitive cycle. The practical comparison is performed applying the same experiment in all architectures, collecting some data and comparing them using a set of software quality metrics as a basis. The aim is to emphasize similarities and differences among the models and implementations, with the purpose to advise a newcomer on how to choose the appropriated architecture for an applicationMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Peripersonal Space in the Humanoid Robot iCub

Developing behaviours for interaction with objects close to the body is a primary goal for any organism to survive in the world. Being able to develop such behaviours will be an essential feature in autonomous humanoid robots in order to improve their integration into human environments. Adaptable spatial abilities will make robots safer and improve their social skills, human-robot and robot-robot collaboration abilities. This work investigated how a humanoid robot can explore and create action-based representations of its peripersonal space, the region immediately surrounding the body where reaching is possible without location displacement. It presents three empirical studies based on peripersonal space findings from psychology, neuroscience and robotics. The experiments used a visual perception system based on active-vision and biologically inspired neural networks. The first study investigated the contribution of binocular vision in a reaching task. Results indicated the signal from vergence is a useful embodied depth estimation cue in the peripersonal space in humanoid robots. The second study explored the influence of morphology and postural experience on confidence levels in reaching assessment. Results showed that a decrease of confidence when assessing targets located farther from the body, possibly in accordance to errors in depth estimation from vergence for longer distances. Additionally, it was found that a proprioceptive arm-length signal extends the robot’s peripersonal space. The last experiment modelled development of the reaching skill by implementing motor synergies that progressively unlock degrees of freedom in the arm. The model was advantageous when compared to one that included no developmental stages. The contribution to knowledge of this work is extending the research on biologically-inspired methods for building robots, presenting new ways to further investigate the robotic properties involved in the dynamical adaptation to body and sensing characteristics, vision-based action, morphology and confidence levels in reaching assessment.CONACyT, Mexico (National Council of Science and Technology

Mental Imagery in Humanoid Robots

Mental imagery presents humans with the opportunity to predict prospective happenings based on own intended actions, to reminisce occurrences from the past and reproduce the perceptual experience. This cognitive capability is mandatory for human survival in this folding and changing world. By means of internal representation, mental imagery offers other cognitive functions (e.g., decision making, planning) the possibility to assess information on objects or events that are not being perceived. Furthermore, there is evidence to suggest that humans are able to employ this ability in the early stages of infancy. Although materialisation of humanoid robot employment in the future appears to be promising, comprehensive research on mental imagery in these robots is lacking. Working within a human environment required more than a set of pre-programmed actions. This thesis aims to investigate the use of mental imagery in humanoid robots, which could be used to serve the demands of their cognitive skills as in humans. Based on empirical data and neuro-imaging studies on mental imagery, the thesis proposes a novel neurorobotic framework which proposes to facilitate humanoid robots to exploit mental imagery. Through conduction of a series of experiments on mental rotation and tool use, the results from this study confirm this potential. Chapters 5 and 6 detail experiments on mental rotation that investigate a bio-constrained neural network framework accounting for mental rotation processes. They are based on neural mechanisms involving not only visual imagery, but also affordance encoding, motor simulation, and the anticipation of the visual consequences of actions. The proposed model is in agreement with the theoretical and empirical research on mental rotation. The models were validated with both a simulated and physical humanoid robot (iCub), engaged in solving a typical mental rotation task. The results show that the model is able to solve a typical mental rotation task and in agreement with data from psychology experiments, they also show response times linearly dependent on the angular disparity between the objects. Furthermore, the experiments in chapter 6 propose a novel neurorobotic model that has a macro-architecture constrained by knowledge on brain, which encompasses a rather general mental rotation mechanism and incorporates a biologically plausible decision making mechanism. The new model is tested within the humanoid robot iCub in tasks requiring to mentally rotate 2D geometrical images appearing on a computer screen. The results show that the robot has an enhanced capacity to generalize mental rotation of new objects and shows the possible effects of overt movements of the wrist on mental rotation. These results indicate that the model represents a further step in the identification of the embodied neural mechanisms that might underlie mental rotation in humans and might also give hints to enhance robots' planning capabilities. In Chapter 7, the primary purpose for conducting the experiment on tool use development through computational modelling refers to the demonstration that developmental characteristics of tool use identified in human infants can be attributed to intrinsic motivations. Through the processes of sensorimotor learning and rewarding mechanisms, intrinsic motivations play a key role as a driving force that drives infants to exhibit exploratory behaviours, i.e., play. Sensorimotor learning permits an emergence of other cognitive functions, i.e., affordances, mental imagery and problem-solving. Two hypotheses on tool use development are also conducted thoroughly. Secondly, the experiment tests two candidate mechanisms that might underlie an ability to use a tool in infants: overt movements and mental imagery. By means of reinforcement learning and sensorimotor learning, knowledge of how to use a tool might emerge through random movements or trial-and-error which might reveal a solution (sequence of actions) of solving a given tool use task accidentally. On the other hand, mental imagery was used to replace the outcome of overt movements in the processes of self-determined rewards. Instead of determining a reward from physical interactions, mental imagery allows the robots to evaluate a consequence of actions, in mind, before performing movements to solve a given tool use task. Therefore, collectively, the case of mental imagery in humanoid robots was systematically addressed by means of a number of neurorobotic models and, furthermore, two categories of spatial problem solving tasks: mental rotation and tool use. Mental rotation evidently involves the employment of mental imagery and this thesis confirms the potential for its exploitation by humanoid robots. Additionally, the studies on tool use demonstrate that the key components assumed and included in the experiments on mental rotation, namely affordances and mental imagery, can be acquired by robots through the processes of sensorimotor learning.Ministry of Science and Technology, the Thai Governmen