Dynamic field theory (DFT): applications in Cognitive Science and Robotics

Review article about Dynamic Field theory and applications in cognitive science and roboticseuCognition : the European Network for Advancement of Artificial Cognitive System

Bridging the gap: a model of common neural mechanisms underlying the Fröhlich effect, the flash-lag effect, and the representational momentum effect

In recent years, the study and interpretation of mislocalization phenomena observed with moving objects have caused an intense debate about the processing mechanisms underlying the encoding of position. We use a neurophysiologically plausible recurrent network model to explain visual illusions that occur at the start, midposition, and end of motion trajectories known as the Fröhlich, the flash-lag, and the representational momentum effect, respectively. The model implements the idea that trajectories are internally represented by a traveling activity wave in position space, which is essentially shaped by local feedback loops within pools of neurons. We first use experimentally observed trajectory representations in the primary visual cortex of cat to adjust the spatial ranges of lateral interactions in the model.We then show that the readout of the activity profile at adequate points in time during the build-up, midphase, and decay of the wave qualitatively and quantitatively explain the known dependence of the mislocalization errors on stimulus attributes such as contrast and speed. We conclude that cooperative mechanisms within the network may be responsible for the three illusions, with a possible intervention of top-down influences that modulate the efficacy of the lateral interactions.Deutscher Akademischer Austauschdienst (DAAD) / Conselho de Reitores das Universidades Portuguesas (CRUP) - As Acções Integradas Luso - AlemãsBundesministerium für Bildung und Forschungthe (BMBF

Robust persistent activity in neural fields with asymmetric connectivity

Modeling studies have shown that recurrent interactions within neural networks are capable of self-sustaining non-uniform activity profiles. These patterns are thought to be the neural basis of working memory. However, the lack of robustness challenge this view as already small deviations from the assumed interaction symmetry destroy the attractor state. Here we analyze attractor states of a neural field model composed of bistable neurons. We show the existence of self-stabilized patterns that robustly represent the cue position in the presence of a substantial asymmetry in the connection profile. Using approximation techniques we derive an explicit expression for a threshold value describing the transition to a traveling activity wave

The dynamic neural field approach to cognitive robotics

This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitr underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner’s action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping–placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work.European Commission fp6-IST2, project no. 00374

Implementing Bayes’ rule with neural fields

Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by non-Bayesian mechanisms such as learning and memory.European Union Joint-Action Science and Technology Project (IST-FP6-003747

On observational learning of hierarchies in sequential tasks: a dynamic neural field model

Many of the tasks we perform during our everyday lives are achieved through sequential execution of a set of goal-directed actions. Quite often these actions are organized hierarchically, corresponding to a nested set of goals and subgoals. Several computational models address the hierarchical execution of goal directed actions by humans. However, the neural learning mechanisms supporting the temporal clustering of goal-directed actions in a hierarchical structure remain to a large extent unexplained. In this paper we investigate in simulations, of a dynamic neural field (DNF) model, biologically-based learning and adaptation mechanisms that can provide insight into the development of hierarchically organized internal representations of naturalistic tasks. In line with recent experimental evidence from observational learning studies, the DNF model implements the idea that prediction errors play a crucial role for grouping fine-grained events into larger units. Our ultimate goal is to use the model to endow the humanoid robot ARoS with the capability to learn hierarchies in sequential tasks, and to use that knowledge to enable efficient collaborative joint tasks with human partners. For testing the ability of the system to deal with the real-time constraints of a learning-by-demonstration paradigm we use the same assembly task from our previous work on human-robot collaboration. The model provides some insights on how hierarchically structured task representations can be learned and on how prediction errors made by the robot and signaled by the demonstrator can be used to control such process.FP7 project NETT // Portuguese FCT Grant SFRH/BD/48529/2008, financed by POPH-QREN-Type 4.1-Advanced Training, co-funded by the European Social Fund and national funds from MEC; (2) FEDER Funds through Competitivity Factors Operational Program - COMPETE and National Funds by FCT Portuguese Science and Technology Foundation under the Project FCOMP-01-0124-FEDER-022674. (2) Project NETT: Neural Engineering Transformative Technologies, EU-FP7 ITN proj. nr. 28914

Integrating verbal and nonverbal communication in a dynamic neural field architecture for human–robot interaction

How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer’s motor system are crucially involved in our ability to understand actions of others’, to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human–robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the human–robot team has to construct toy objects from their components. The experiments focus on the robot’s capacity to anticipate the user’s needs and to detect and communicate unexpected events that may occur during joint task execution.Fundação para a Ciência e a Tecnologia (FCT) - Bolsa POCI/V.5/A0119/2005 and CONC-REEQ/17/2001European Commission through the project JAST (IP-003747

Human-like arm motion generation: a review

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

A dynamic field model of ordinal and timing properties of sequential events

Recent evidence suggests that the neural mechanisms underlying memory for serial order and interval timing of sequential events are closely linked. We present a dynamic neural field model which exploits the existence and stability of multi-bump solutions with a gradient of activation to store serial order. The activation gradient is achieved by applying a state-dependent threshold accommodation process to the firing rate function. A field dynamics of lateral inhibition type is used in combination with a dynamics for the baseline activity to recall the sequence from memory. We show that depending on the time scale of the baseline dynamics the precise temporal structure of the original sequence may be retrieved or a proactive timing of events may be achievedFundação para a Ciência e a Tecnologia (FCT) - Bolsa SFRH/BD/41179/200

Coordinated transportation with minimal explicit communication between robots

PreprintWe propose and demonstrate how attractor dynamics can be used to design and implement a distributed dynamic control architecture that enables a team of two robots, without force/torque sensors and equipped solely with low-level sensors, to carry a long object and simultaneously avoid obstacles. The explicit required communication between robots is minimal. The robots have no prior knowledge of their environment. Experimental results in indoor environments show that if parameter values are chosen within reasonable ranges then the overall system works quite well even in cluttered environments. The robots’ behavior is stable and the generated trajectories are smooth