One model to drift them all: Physics-informed conditional diffusion model for driving at the limits
Enabling autonomous vehicles to reliably operate at the limits of handling—where tire forces
are saturated—would improve their safety, particularly in scenarios like emergency obstacle …
are saturated—would improve their safety, particularly in scenarios like emergency obstacle …
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control
Accurate models of robot dynamics are critical for safe and stable control and generalization
to novel operational conditions. Hand-designed models, however, may be insufficiently …
to novel operational conditions. Hand-designed models, however, may be insufficiently …
Learning of Hamiltonian dynamics with reproducing Kernel Hilbert spaces
T Smith, O Egeland - 2024 European Control Conference (ECC …, 2024 - ieeexplore.ieee.org
This paper presents a method for learning Hamil-tonian dynamics from a limited set of data
points. The Hamil-tonian vector field is found by regularized optimization over a reproducing …
points. The Hamil-tonian vector field is found by regularized optimization over a reproducing …
Data-Driven Closed-Loop Reachability Analysis for Nonlinear Human-in-the-Loop Systems Using Gaussian Mixture Model
This article presents data-driven algorithms to perform the reachability analysis of nonlinear
human-in-the-loop (HITL) systems. Such systems require consideration of the human control …
human-in-the-loop (HITL) systems. Such systems require consideration of the human control …
A model reference adaptive controller based on operator-valued kernel functions
This paper extends recent results on model reference adaptive control using reproducing
kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input …
kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input …
Engineering AI systems and AI for engineering: compositionality and physics in learning
C Neary - 2024 - repositories.lib.utexas.edu
How can we transform artificial intelligence (AI) capabilities into engineering systems? That
is, how can we engineer AI systems within budget constraints, certify them with respect to …
is, how can we engineer AI systems within budget constraints, certify them with respect to …
Data-Driven Superstabilization of Linear Systems Under Quantization
This paper focuses on the stabilization and regulation of linear systems affected by
quantization in state-transition data and in actuated input. The observed data are composed …
quantization in state-transition data and in actuated input. The observed data are composed …
Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and Random Features
T Smith, O Egeland - arxiv preprint arxiv:2404.07703, 2024 - arxiv.org
A method for learning Hamiltonian dynamics from a limited and noisy dataset is proposed.
The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) …
The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) …
Refining Human-Centered Autonomy Using Side Information
AJ Thorpe - arxiv preprint arxiv:2305.05607, 2023 - arxiv.org
Data-driven algorithms for human-centered autonomy use observed data to compute
models of human behavior in order to ensure safety, correctness, and to avoid potential …
models of human behavior in order to ensure safety, correctness, and to avoid potential …
Data-Driven Stochastic Optimal Control Using Hilbert Space Embeddings of Distributions
AJ Thorpe - 2023 - search.proquest.com
Autonomous systems are increasingly being deployed in complex environments subject to
real-world uncertainty. For such systems, it may be exceptionally difficult or even impossible …
real-world uncertainty. For such systems, it may be exceptionally difficult or even impossible …