4 citations · 6 across the 7 of their papers we have counts for
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cs.LG2024
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…
cs.RO2024★ 1 cited
Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models
Sigmund H. Høeg, Yilun Du, Olav Egeland
Diffusion models have seen rapid adoption in robotic imitation learning, enabling autonomous execution of complex dexterous tasks. However, action synthesis is often slow, requirin…
cs.LG2024★ 4 cited
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…