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Inspired by the longitudinal data of von Hofsten [1], we provide a neural process model of autonomously learning to direct pre-reaches toward visual objects. We build on an earlier neural dynamics account of pre-reaching [2], in which the elementary behaviors of visual fixation, reaching toward targets, returning to a resting position, closing, and opening the hand are tied to perceptual inputs and...
We present a neuro-dynamic model of looking, reaching, and grasping movements in infants in three pre-reaching phases. We attribute the evolution from pre-reaches to their suppression and subsequent re-emergence reported in a longitudinal study of von Hofsten [1] to the development of the sequential organization of movements, through which a set of elementary movements (visual fixation, reaching,...
We present a neuro-dynamic architecture for the generation of movement of the hand toward a visual target that integrates movement planning based on visual input, movement initiation and termination, the generation of the time courses of virtual trajectories of the hand in Cartesian space, and their transformation into virtual joint trajectories and muscle forces. The architecture captures properties...
We present a neural dynamics architecture for robotic grasping of novel objects. It closes the perception-action loop by integrating perceptual processes such as scene exploration, pose estimation, and shape classification with movement generation to reach and grasp a target object. Inspired by theories of human embodied cognition, this is achieved by interconnected dynamical systems, whose dynamical...
Neural dynamics offer a theoretical and computational framework, in which cognitive architectures may be developed, which are suitable both to model psychophysics of human behaviour and to control robotic behaviour. Recently, we have introduced reinforcement learning in this framework, which allows an agent to learn goal-directed sequences of behaviours based on a reward signal, perceived at the end...
To address how different movement behaviors may be timed to sensory events while being flexibly organized in sequence, we propose a neural dynamic model of timed movement organization. Two layers of neural dynamics control the activation and de-activation of different elementary movements, while a third layer uses stable limit cycle oscillators to generate timed movement trajectories. Both the organization...
The metaphor of Dynamical Systems has influenced how psychologists, developmental scientists, cognitive scientists, and neuroscientists think about sensori-motor processes and their development [1]. The initial emphasis on motor behavior was expanded when the concept of dynamic activation fields provided access to embodied cognition [2]. Dynamical Field Theory (DFT) offers a framework for thinking...
We study four established reactive approaches that can be implemented on computationally weak hardware with the goal of minimizing oscillatory movements to reduce the energetic cost of robot navigation. In this regard, we examine the smoothness and variability of the control action in our analysis. Sensor noise, including the large variance of GPS estimates, is evaluated. Care is taken to make the...
We present a neural dynamics architecture for grasping that integrates perceptual processes of scene exploration, object selection and classification, and grasp pose estimation with motor processes such as planning and controlling reach and grasp movements. Inspired by theories of human embodied cognition, the entire architecture is essentially one big dynamical system from which discrete events such...
We propose a model that autonomously generates and flexibly organizes sequences of timed actions. The timing of the movements is controlled by non-linear oscillators. Their activation and deactivation is organized by a hierarchical neural-dynamic architecture. We demonstrate the features of our model in an exemplary robotic task where the manipulator arm keeps hitting a ball up an inclined plane....
The DN-SARSA(λ) model provides a framework which shows how computational learning algorithms can be incorporated into a continuous neural-dynamical model. This enables autonomous learning and acting in continuous and dynamic environments, a challenge that is easily overlooked when formalizing the learning problem in discretized spaces without accounting for their coupling to sensory-motor dynamics.
Robotic agents that interact with humans and perform complex, everyday tasks in natural environments will require a system to autonomously organize their behavior. Current systems for robotic behavioral organization typically abstract from the low-level sensory-motor embodiment of the robot, leading to a gap between the level at which a sequence of actions is planned and the levels of perception and...
Spatial language is a privileged channel of human-robot interaction. Here, we extend a neural-dynamic architecture for grounded spatial language in three ways. First, we introduce autonomous selection between viewer-centered and intrinsic reference frames, using an estimation of the reference object orientation to determine its intrinsic axes. Second, we employ an orientation estimation dynamics to...
How agents generate meaningful sequences of actions in natural environments is one of the most challenging problems in studies of natural cognition and in the design of artificial cognitive systems. Each action in a sequence must contribute to the behavioral objective, while at the same time satisfying constraints that arise from the environment, the agent's embodiment, and the agent's behavioral...
We present a neural architecture for scene representation that stores semantic information about objects in the robot's workspace. We show how this representation can be queried both through low-level features such as color and size, through feature conjunctions, as well as through symbolic labels. This is possible by binding different feature dimensions through space and integrating these space-feature...
The movement of autonomous agents in natural environments is restricted by potentially large numbers of constraints. To generate behavior that fulfills all given constraints simultaneously, the attractor dynamics approach to movement generation represents each constraint by a dynamical system with attractors or repellors at desired or undesired values of a relevant variable. These dynamical systems...
We extend the attractor dynamics approach to generate goal-directed movement of a redundant, anthropomorphic arm while avoiding dynamic obstacles and respecting joint limits. To make the robot's movements human-like, we generate approximately straight-line trajectories by using two heading direction angles of the tool-point quite analogously to how movement is represented in the primate central nervous...
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