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

Robot formations: robots allocation and leader-follower pairs

In this paper we focus on the problem of assigning robots to places in a desired formation, considering random initial locations of the robots. Since we use a leader-follower strategy, we also address the task of choosing the leader to each follower. The result is a formation matrix that describes the relation between the robots and the desired formation shape. Simple algorithms are defined, that are based on the minimization of the distances of robots to places in the formation. All these algorithms are implemented in a decentralized way. We assume that communication is possible, but the requirements are of very-low bandwidth.Fundação para a Ciência e a Tecnologia (FCT

Attractor dynamics approach to formation control : theory and application

In this paper we show how non-linear attractor dynamics can be used as a framework to control teams of autonomous mobile robots that should navigate according to a predefined geometric formation. The environment does not need to be known a priori and may change over time. Implicit to the control architecture are some important features such as establishing and moving the formation, split and join of formations (when necessary to avoid obstacles). Formations are defined by a formation matrix. By manipulating this formation matrix it is also possible to switch formations at run time. Examples of simulation results and implementations with real robots (teams of Khepera robots and medium size mobile robots), demonstrate formation switch, static and dynamic obstacle avoidance and split and join formations without the need for any explicit coordination scheme. Robustness against environmental perturbations is intrinsically achieved because the behaviour of each robot is generated as a time series of asymptotically stable states, which contribute to the asymptotic stability of the overall control system.This work was supported through COOP-DYN (POSI/SRI/38051/2001), financed by the Portuguese Foundation for Science and Technology (FCT) and project fp6-IST2 EU-project JAST-Joint Action Science and Technology (project number 003747). We thank Miguel Vaz and Nzoji Hipolito for their help in the implementations with real robots. We would like also to thank the anonymous reviewers for their insightful comments which helped improving the paper

Coordinated transportation of a large object by a team of three robots

Dynamical systems theory in this work is used as a theoretical language and tool to design a distributed control architecture for a team of three robots that must transport a large object and simultaneously avoid collisions with either static or dynamic obstacles. The robots have no prior knowledge of the environment. The dynamics of behavior is defined over a state space of behavior variables, heading direction and path velocity. Task constraints are modeled as attractors (i.e. asymptotic stable states) of the behavioral dynamics. For each robot, these attractors are combined into a vector field that governs the behavior. By design the parameters are tuned so that the behavioral variables are always very close to the corresponding attractors. Thus the behavior of each robot is controlled by a time series of asymptotical stable states. Computer simulations support the validity of the dynamical model architecture.(undefined

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

A dynamic neural field architecture for a pro-active assistant robot

We present a control architecture for non-verbal HRI that allows an assistant robot to have a pro-active and anticipatory behavior. The architecture implements the coordination of actions and goals among the human, that needs help, and the robot as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of the human motor behavior. The robot control architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations with specific functionalities. Different subpopulations encode task relevant information about action means, action goals and context in form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic control architecture is validated in an assistive task in which an anthropomorphic robot acts as a personal assistant of a person with motor impairments. We show that the context dependent mapping from action observation onto appropriate complementary actions allows the robot to cope with dynamically changing situations. This includes adaptation to different users and mutual compensation of physical limitations.Fundação para a Ciência e a Tecnologia (FCT) - POCI/V.5/A0119/2005fp6-IST2 EU-project JAST (proj.nr. 003747

Attractor dynamics generates robot formations: from theory to implementation

We show how non-linear attractor dynamics can be used to implement robot formations in unknown environments. The desired formation geometry is given through a matrix where the parameters in each line (its leader, desired distance and relative orientation to the leader) define the desired pose of a robot in the formation. The parameter values are then used to shape the vector fields of the dynamical systems that generate values for the control variables (i.e. heading direction and path velocity). Then these dynamical systems are tuned such that the control variables are always very close to one of the resultant attractors. The advantage is that the systems are more robust against perturbations because the behavior is generated as a time series of asymptotically stable states. Experimental results (with three Khepera robots) demonstrate the ability of the team to create and stabilize the formation, as well as avoiding obstacles. Flexibility is achieved in that as the senses world changes, the systems may change their planning solutions continuously but also discontinuously (tunning the formation versus split to avoid obstacle).Fundação para a Ciência e a Tecnologia (FCT) - (POSI/SRI/38051/2001

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

La dynamique des attracteurs comme base de génération de comportements en robotique mobile autonome

L’hypothèse centrale de l’approche dynamique en robotique mobile autonome (Schöner et Dose, 1992 ; Schöner, Dose et Engels, 1995 ; Bicho et Schöner, 1997 ; Steinhage et Schöner, 1998 ; Large, Christensen et Bajcy, 1999 ; Bicho, Mallet et Schöner, 2000) est que le comportement moteur ainsi que les représentations pertinentes nécessaires à sa réalisation doivent, d’une part, être générés de façon continue dans le temps, et, d’autre part, résister aux fluctuations ou perturbations auxquelles tout système réel est exposé. Cela conduit à une conception dans laquelle le comportement et les représentations sont les solutions stables (ou attracteurs) d’un ensemble de systèmes dynamiques, qui traduisent en temps réel l’information sensorielle en contraintes graduées et intégrables. De multiples attracteurs peuvent co-exister en présence de la même situation sensorielle. C’est l’état interne du système autonome qui décidera quel attracteur sera choisi. Le changement du nombre et/ou de la nature des attracteurs à travers des instabilités (ou bifurcations) permet au système autonome de se configurer de manière flexible selon le contexte sensoriel instantané. L’approche est ici présentée au niveau de la génération de comportements moteurs. Dans ce cas, des variables « comportementales » représentent directement un continuum d’états physiques du système qui sont générés par des systèmes de contrôle conventionnels. Par exemple, une variable représentant la direction dans laquelle un véhicule se déplace, peut évoluer dans le temps grâce à un système dynamique qui intègre les contraintes « acquisition de cibles » et « évitement d’obstacles ». La fusion ou sélection parmi ces contraintes est réalisée au moyen d’une dynamique non linéaire bien maîtrisée

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