244 research outputs found
Adaptive timing in a dynamic field architecture for natural humanârobot interactions
A close temporal coordination of actions and goals is crucial for natural and fluent humanârobot interactions in collaborative tasks. How to endow an autonomous robot with a basic temporal cognition capacity is an open question. In this paper, we present a neurodynamics approach based on the theoretical framework of dynamic neural fields (DNF) which assumes that timing processes are closely integrated with other cognitive computations. The continuous evolution of neural population activity towards an attractor state provides an implicit sensation of the passage of time. Highly flexible sensorimotor timing can be achieved through manipulations of inputs or initial conditions that affect the speed with which the neural trajectory evolves. We test a DNF-based control architecture in an assembly paradigm in which an assistant hands over a series of pieces which the operator uses among others in the assembly process. By watching two experts, the robot first learns the serial order and relative timing of object transfers to subsequently substitute the assistant in the collaborative task. A dynamic adaptation rule exploiting a perceived temporal mismatch between the expected and the realized transfer timing allows the robot to quickly adapt its proactive motor timing to the pace of the operator even when an additional assembly step delays a handover. Moreover, the self-stabilizing properties of the population dynamics support the fast internal simulation of acquired task knowledge allowing the robot to anticipate serial order errorsThis work is financed by national funds through FCT â Fundação para a CiĂȘncia e a Tecnologia, I.P., within the scope of the projects ââNEUROFIELDââ (Ref PTDC/MAT-APL/31393/2017), ââI-CATER â Intelligent Robotic Coworker Assistant for Industrial Tasks with an Ergonomics Rationaleââ (Ref PTDC/EEI-ROB/3488/2021) and R&D Units Project Scope: UIDB/00319/2020 â ALGORITMI Research Centre
Temporal Cognition: A Key Ingredient of Intelligent Systems
Experiencing the flow of time is an important capacity of biological systems that is involved in many ways in the daily activities of humans and animals. However, in the field of robotics, the key role of time in cognition is not adequately considered in contemporary research, with artificial agents focusing mainly on the spatial extent of sensory information, almost always neglecting its temporal dimension. This fact significantly obstructs the development of high-level robotic cognitive skills, as well as the autonomous and seamless operation of artificial agents in human environments. Taking inspiration from biological cognition, the present work puts forward time perception as a vital capacity of artificial intelligent systems and contemplates the research path for incorporating temporal cognition in the repertoire of robotic skills
Composition and the search for selfâawareness
Composition studies saw several cogent criticisms of expressivism in the late 1980s and early 1990s. Some scholars assume that those criticisms discredited expressivism in composition studies, ending the focus on assignments that ask students to write personal, supposedly introspective papers that were believed to lead to selfâawareness and selfâidentity. Even so, recent research suggests that the expressivist pedagogical orientation is still widely used in writing classes across the US. Joshua Hilst (2012) sought to rehabilitate expressivism by drawing on the work of philosopher Giles Deleuze, arguing that neoâexpressivism provides a palliative to those criticisms. In this regard, Hilstâs analysis follows the current trend of applying Deleuzeâs philosophy to a variety of fields. The present analysis therefore consists of two parts, both with pedagogical implications. First, it examines Deleuzeâs work and illustrates how his neoâexpressivism and views on writing are incongruent with the expressivism applied in composition studies. Second, it examines the psychological research on introspection and selfâ awareness that has demonstrated with considerable consistency the opacity of mental processes and the difficulty associated with gaining any sense of selfâawareness or selfâidentity
Prosody and synchronization in cognitive neuroscience
We introduce our methodological study with a short review of the main literature on embodied language, including some recent studies in neuroscience. We investigated this component of natural language using Recurrence Quantification Analysis (RQA). RQA is a relatively new statistical methodology, particularly effective in complex systems. RQA provided a reliable quantitative description of recurrences in text sequences at the orthographic level. In order to provide examples of the potential impact of this methodology, we used RQA to measure structural coupling and synchronization in natural and clinical verbal interactions. Results show the efficacy of this methodology and possible implications
Dynamical system with plastic self-organized velocity field as an alternative conceptual model of a cognitive system
It is well known that architecturally the brain is a neural network, i.e. a collection of many relatively simple units coupled flexibly. However, it has been unclear how the possession of this architecture enables higher-level cognitive functions, which are unique to the brain. Here, we consider the brain from the viewpoint of dynamical systems theory and hypothesize that the unique feature of the brain, the self-organized plasticity of its architecture, could represent the means of enabling the self-organized plasticity of its velocity vector field. We propose that, conceptually, the principle of cognition could amount to the existence of appropriate rules governing self-organization of the velocity field of a dynamical system with an appropriate account of stimuli. To support this hypothesis, we propose a simple non-neuromorphic mathematical model with a plastic self-organized velocity field, which has no prototype in physical world. This system is shown to be capable of basic cognition, which is illustrated numerically and with musical data. Our conceptual model could provide an additional insight into the working principles of the brain. Moreover, hardware implementations of plastic velocity fields self-organizing according to various rules could pave the way to creating artificial intelligence of a novel type
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Beyond human: deep learning, explainability and representation
This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of âalgorithmic thoughtâ. Research in deep learning is its case study. This artificial intelligence (AI) technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture tackle the possibility to âre-presentâ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability (originally developed in the philosophy of science) to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic âthinkingâ operations
Temporal structure of neural oscillations underlying sensorimotor coordination: a theoretical approach with evolutionary robotics
The temporal structure of neural oscillations has become a widespread hypothetical
\mechanism" to explain how neurodynamics give rise to neural functions. Despite the
great number of empirical experiments in neuroscience and mathematical and computa-
tional modelling investigating the temporal structure of the oscillations, there are still
few systematic studies proposing dynamical explanations of how it operates within closed
sensorimotor loops of agents performing minimally cognitive behaviours. In this thesis
we explore this problem by developing and analysing theoretical models of evolutionary
robotics controlled by oscillatory networks. The results obtained suggest that: i) the in-
formational content in an oscillatory network about the sensorimotor dynamics is equally
distributed throughout the entire range of phase relations; neither synchronous nor desyn-
chronous oscillations carries a privileged status in terms of informational content in relation
to an agent's sensorimotor activity; ii) although the phase relations of oscillations with
a narrow frequency difference carry a relatively higher causal relevance than the rest of
the phase relations to sensorimotor coordinations, overall there is no privileged functional
causal contribution to either synchronous or desynchronous oscillations; and iii) oscilla-
tory regimes underlying functional behaviours (e.g. phototaxis, categorical perception) are
generated and sustained by the agent's sensorimotor loop dynamics, they depend not only
on the dynamic structure of a sensory input but also on the coordinated coupling of the
agent's motor-sensory dynamics. This thesis also contributes to the Coordination Dynam-
ics framework (Kelso, 1995) by analysing the dynamics of the HKB (Haken-Kelso-Bunz)
equation within a closed sensorimotor loop and by discussing the theoretical implications
of such an analysis. Besides, it contributes to the ongoing philosophical debate about
whether actions are either causally relevant or a constituent of cognitive functionalities by
bringing this debate to the context of oscillatory neurodynamics and by illustrating the
constitutive notion of actions to cognition
The role of robotics and AI in technologically mediated human evolution: a constructive proposal
This paper proposes that existing computational modeling research programs may be combined into platforms for the information of public policy. The main idea is that computational models at select levels of organization may be integrated in natural terms describing biological cognition, thereby normalizing a platform for predictive simulations able to account for both human and environmental costs associated with different action plans and institutional arrangements over short and long time spans while minimizing computational requirements. Building from established research programs, the proposal aims to take advantage of current momentum in the direction of the integration of the cognitive with social and natural sciences, reduce start-up costs and increase speed of development. These are all important upshots given rising unease over the potential for AI and related technologies to shape the world going forward
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