7 papers
MP-MPPI: A Motion Primitive Guided Sampling-Based Optimizer for Model Predictive Control
Marlon Mathisen, Aksel Vaaler, Olav Egeland +1
This paper proposes a novel method that extends the Model Predictive Path Integral (MPPI) method with motion primitives for additional structured sampling, which enhances the conve…
Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Sigmund Hennum Høeg, Aksel Vaaler, Chaoqi Liu +2
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models…
Nonparametric adaptive payload tracking for an offshore crane
Torbjørn Smith, Olav Egeland
A nonparametric adaptive controller is proposed for crane control where the payload tracks a desired trajectory with feedback from the payload position. The controller is based on…
Learning dissipative Hamiltonian dynamics with reproducing kernel Hilbert spaces and random Fourier features
Torbjørn Smith, Olav Egeland
This paper presents a new method for learning dissipative Hamiltonian dynamics from a limited and noisy dataset. The method uses the Helmholtz decomposition to learn a vector field…
Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and Random Features
Torbjørn Smith, Olav Egeland
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) o…
Learning of Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces
Torbjørn Smith, Olav Egeland
This paper presents a method for learning Hamiltonian dynamics from a limited set of data points. The Hamiltonian vector field is found by regularized optimization over a reproduci…