Dynamic Field Theory Research Papers (original) (raw)

2025, Spatial Resonance and Field-State Modulation for Non-Propellant Motion Systems

This paper introduces a theoretical control model capable of inertial displacement without the use of propellant, combustion, or traditional reaction mass. Built upon the foundation of field resonance and harmonic feedback systems, the framework outlines how a localized energy envelope can be modulated to create controlled, directional motion through dynamic field interactions. The result is a closed-loop inertial architecture that achieves self-stabilized thrust purely through internal phase resonance and spatial gradient alignment. While remaining within the boundary of known physics, this architecture redefines motion itself-suggesting that inertia, once thought untouchable, is not only tunable but controllable.

2025

For decades, the question of life’s origin has been examined through the lens of chemical chance, evolutionary necessity, and the thermodynamics of biological complexity. Abiogenesis, the prevailing hypothesis, suggests that life arose from a primordial soup through stochastic molecular interactions. However, through my own cogitations and retroductive reasoning, I have come to a different conclusion—one that, when fully explored, presents greater verisimilitude than any other hypothesis.

2024, Neural Computing and Applications

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human-robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that applies brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over

2024, Neural Computing and Applications

2023, Lecture Notes in Computer Science

A dilemma arises when sequence generation is considered in embodied autonomous agents. While achieving an individual action goal, the agent must be in a stable state to link to fluctuating and time-varying sensory inputs. To transition to the next goal, the previous state must be released from stability. A previous proposal of a neural dynamics that solves this dilemma by inducing an instability when a "condition of satisfaction" signals that an action goal has been reached does not scale to more complex sequences. We address this limitation by showing how the neural dynamics can be generalized to generate hierarchically structured behaviors. Actions are initiated in a downward stream, their termination is signaled in an upward stream. We analyze the mathematical mechanisms and demonstrate the flexibility of the scheme in simulation.

2023, Proceedings of the Annual Meetings on Phonology

Recent work has shown that lexical items come to take on the phonetic characteristics of the prosodic environments in which they are typically produced, a phenomenon referred to as “leaky prosody”. Focusing on pitch patterns in Mandarin, we show that leaky prosody can be derived from a flat (i.e., non-transformational, non-optimizing) model of speech production. Formalized using Dynamic Field Theory, in our model, lexical, phonological, and prosodic inputs each exert forces on a Dynamic Neural Field representing pitch. Notably, the forces exerted by these inputs reflect surface distributions in a large corpus of spontaneous speech. Our simulations showed that the flat model derives the short timescale effect of prosodic prominence on pitch production as well as the longer timescale effect of leaky prosody. By updating lexical items based on surface phonetic form, words that are consistently produced in high/low prosodic prominence positions take on the phonetic characteristics of those environments.

2023, Robotics and Autonomous Systems

In this paper we present a robot control architecture for learning by imitation which takes inspiration from recent discoveries in action observation/execution experiments with humans and other primates. The architecture implements two basic processing principles: (1) imitation is primarily directed toward reproducing the outcome of an observed action sequence rather than reproducing the exact action means, and (2) the required capacity to understand the motor intention of another agent is based on motor simulation. The control architecture is validated in a robot system imitating in a goal-directed manner a grasping and placing sequence displayed by a human model. During imitation, skill transfer occurs by learning and representing appropriate goal-directed sequences of motor primitives. The robustness of the goal-directed organization of the controller is tested in the presence of incomplete visual information and changes in environmental constraints.

2022, Neural Computing and Applications

2022

The implementation of sequence learning in robotic platforms o ers several challenges. Deciding when to stop one action and continue to the next requires a balance between stability of sensory information and, of course, the knowledge about what action is required next. The work presented here proposes a starting point for the successful execution and learning of dynamic sequences. Making use of the NAO humanoid platform we propose a mathematical model based on dynamic field theory and reinforcement learning methods for obtaining and performing a sequence of elementary motor behaviors. Results from the comparison of two reinforcement learning methods applied to sequence generation, for both simulation and implementation, are provided.

2021, 2012 12th Ieee Ras International Conference on Humanoid Robots

Robotic researchers face fundamental challenges when designing autonomous humanoid robots, which are able to interact with real dynamic environments. In such unstructured environments, the robot has to autonomously segment objects, detect and categorize relevant situations, decide when to initiate and terminate actions. As humans are very good in these tasks, inspiration from models of human sensory-motor and cognitive processes may help design more flexible and autonomous robotic control architectures. Recently, we have extended a neurallyinspired model for sequential organization with a representation of hierarchies of behaviors. Here, we implement this model on a robotic platform and demonstrate its functionality under constraints of a real-world implementation. The architecture generates hierarchically organized behavioral sequences on the Aldebaran's humanoid robot NAO. The key dynamic components of serial organization-such as the intention, condition of satisfaction (CoS), and interactions within the hierarchy-are coupled to robotic sensors and motors and bring about flexible and autonomous behavior. We also demonstrate how continuous in time neural-dynamic parts of the controller may be seamlessly integrated with preprogramed algorithmic behaviors, introducing flexibility, autonomy, and ability to learn, while avoiding unnecessary complexity of the architecture.

2016

The problem of creation of Unitary Field theory, or the Theory of Everything, which the Einstein was so eager to solve by means of physics, remained unsolved since it is solvable only by means of the Word: because the Word, according to Bible — is the God and «the God has»: Universe, His Child. This particular solution is given by the linguistic work about the Existing, offered by me to you and based on the Pressing method, the founder of which is Dionysus (Bacchus) — the ancient's god of the Word, Moon and Wine. // Познать Мир, Божий Храм - есть познать Слово нам. Слово - это Луна.

2016, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

Robotic researchers face fundamental challenges when designing autonomous humanoid robots, which are able to interact with real dynamic environments. In such unstructured environments, the robot has to autonomously segment objects, detect and categorize relevant situations, decide when to initiate and terminate actions. As humans are very good in these tasks, inspiration from models of human sensory-motor and cognitive processes may help design more flexible and autonomous robotic control architectures. Recently, we have extended a neurallyinspired model for sequential organization with a representation of hierarchies of behaviors. Here, we implement this model on a robotic platform and demonstrate its functionality under constraints of a real-world implementation. The architecture generates hierarchically organized behavioral sequences on the Aldebaran's humanoid robot NAO. The key dynamic components of serial organization -such as the intention, condition of satisfaction (CoS), and interactions within the hierarchy -are coupled to robotic sensors and motors and bring about flexible and autonomous behavior. We also demonstrate how continuous in time neural-dynamic parts of the controller may be seamlessly integrated with preprogramed algorithmic behaviors, introducing flexibility, autonomy, and ability to learn, while avoiding unnecessary complexity of the architecture.

2015

Resolving relational spatial phrases requires that a coherent mapping emerges between a visual scene and a triad of two objects and a relational term. We present a theoretical account that solves this problem based on neural principles. A neural dynamic architecture represents perceptual information in activation fields that make detection and selection decisions through neural interaction. Activation nodes and their connectivity to the perceptual fields represent concepts. Dynamic instabilities enable the autonomous sequential organization of the processing steps needed to resolve relational spatial phrases. These include bringing visual objects into the attentional foreground, performing spatial transformations, and making matching decisions. We demonstrate how the neural architecture may autonomously test different hypotheses to resolve relational spatial phrases. We discuss how this neural process account relates to existing theoretical perspectives and how to move beyond the entry point sketched here.

2015

Robotic researchers face fundamental challenges when designing autonomous humanoid robots, which are able to interact with real dynamic environments. In such unstructured environments, the robot has to autonomously segment objects, detect and categorize relevant situations, decide when to initiate and terminate actions. As humans are very good in these tasks, inspiration from models of human sensory-motor and cognitive processes may help design more flexible and autonomous robotic control architectures. Recently, we have extended a neurallyinspired model for sequential organization with a representation of hierarchies of behaviors. Here, we implement this model on a robotic platform and demonstrate its functionality under constraints of a real-world implementation. The architecture generates hierarchically organized behavioral sequences on the Aldebaran's humanoid robot NAO. The key dynamic components of serial organization -such as the intention, condition of satisfaction (CoS), and interactions within the hierarchy -are coupled to robotic sensors and motors and bring about flexible and autonomous behavior. We also demonstrate how continuous in time neural-dynamic parts of the controller may be seamlessly integrated with preprogramed algorithmic behaviors, introducing flexibility, autonomy, and ability to learn, while avoiding unnecessary complexity of the architecture.

2015, Artificial Neural Networks and Machine Learning–ICANN 2012

2014

A dilemma arises when sequence generation is implemented on embodied autonomous agents. While achieving an individual action goal, the agent must be in a stable state to link to fluctuating and timevarying sensory inputs. To transition to the next goal, the previous state must be released from stability. A previous proposal of a neural dynamics solved this dilemma by inducing an instability when a "condition of satisfaction" signals that an action goal has been reached. The required structure of dynamic coupling limited the complexity and flexibility of sequence generation, however. We address this limitation by showing how the neural dynamics can be generalized to generate hierarchically structured behaviors. Directed couplings downward in the hierarchy initiate chunks of actions, directed couplings upward in the hierarchy signal their termination. We analyze the mathematical mechanisms and demonstrate the flexibility of the scheme in simulation.

2013

Abstract 1. We propose a neural dynamic model that specifies how low-level visual processes can be integrated with higher level cognition to achieve flexible spatial language behaviors. This model uses real-word visual input that is... more

2012

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 history. In this paper, we introduce a neural-dynamic architecture that enables selection of an appropriate action for a given task in a particular environment and is open to learning. We use the same framework of neural dynamics for all processes from perception, to representation and motor planning as well as behavioral organization. This facilitates integration and flexibility. The neural dynamic representations of particular behaviors emerge on the fly from the interplay between task and environment inputs as well as behavioral history. All behavioral states are attractors of the neural dynamics, whose instabilities lead to behavioral switches. As a result, behavioral organization is robust in the face of noisy and unreliable sensory information.

2012