3,169 research outputs found
Life-Space Foam: a Medium for Motivational and Cognitive Dynamics
General stochastic dynamics, developed in a framework of Feynman path
integrals, have been applied to Lewinian field--theoretic psychodynamics,
resulting in the development of a new concept of life--space foam (LSF) as a
natural medium for motivational and cognitive psychodynamics. According to LSF
formalisms, the classic Lewinian life space can be macroscopically represented
as a smooth manifold with steady force-fields and behavioral paths, while at
the microscopic level it is more realistically represented as a collection of
wildly fluctuating force-fields, (loco)motion paths and local geometries (and
topologies with holes). A set of least-action principles is used to model the
smoothness of global, macro-level LSF paths, fields and geometry. To model the
corresponding local, micro-level LSF structures, an adaptive path integral is
used, defining a multi-phase and multi-path (multi-field and multi-geometry)
transition process from intention to goal-driven action. Application examples
of this new approach include (but are not limited to) information processing,
motivational fatigue, learning, memory and decision-making.Comment: 25 pages, 2 figures, elsar
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
Recent approaches for modelling dynamics of physical systems with neural
networks enforce Lagrangian or Hamiltonian structure to improve prediction and
generalization. However, these approaches fail to handle the case when
coordinates are embedded in high-dimensional data such as images. We introduce
a new unsupervised neural network model that learns Lagrangian dynamics from
images, with interpretability that benefits prediction and control. The model
infers Lagrangian dynamics on generalized coordinates that are simultaneously
learned with a coordinate-aware variational autoencoder (VAE). The VAE is
designed to account for the geometry of physical systems composed of multiple
rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the
model learns physical system properties, such as kinetic and potential energy,
which enables long-term prediction of dynamics in the image space and synthesis
of energy-based controllers
Putting energy back in control
A control system design technique using the principle of energy balancing was analyzed. Passivity-based control (PBC) techniques were used to analyze complex systems by decomposing them into simpler sub systems, which upon interconnection and total energy addition were helpful in determining the overall system behavior. An attempt to identify physical obstacles that hampered the use of PBC in applications other than mechanical systems was carried out. The technique was applicable to systems which were stabilized with passive controllers
Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics
A long-running difficulty with conventional game theory has been how to
modify it to accommodate the bounded rationality of all real-world players. A
recurring issue in statistical physics is how best to approximate joint
probability distributions with decoupled (and therefore far more tractable)
distributions. This paper shows that the same information theoretic
mathematical structure, known as Product Distribution (PD) theory, addresses
both issues. In this, PD theory not only provides a principled formulation of
bounded rationality and a set of new types of mean field theory in statistical
physics. It also shows that those topics are fundamentally one and the same.Comment: 17 pages, no figures, accepted for publicatio
Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control
Reliable autonomous navigation requires adapting the control policy of a
mobile robot in response to dynamics changes in different operational
conditions. Hand-designed dynamics models may struggle to capture model
variations due to a limited set of parameters. Data-driven dynamics learning
approaches offer higher model capacity and better generalization but require
large amounts of state-labeled data. This paper develops an approach for
learning robot dynamics directly from point-cloud observations, removing the
need and associated errors of state estimation, while embedding Hamiltonian
structure in the dynamics model to improve data efficiency. We design an
observation-space loss that relates motion prediction from the dynamics model
with motion prediction from point-cloud registration to train a Hamiltonian
neural ordinary differential equation. The learned Hamiltonian model enables
the design of an energy-shaping model-based tracking controller for rigid-body
robots. We demonstrate dynamics learning and tracking control on a real
nonholonomic wheeled robot.Comment: 8 pages, 6 figure
Machine Learning, Quantum Mechanics, and Chemical Compound Space
We review recent studies dealing with the generation of machine learning
models of molecular and solid properties. The models are trained and validated
using standard quantum chemistry results obtained for organic molecules and
materials selected from chemical space at random
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