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Structure-Aware Spatial Kernels for Gaussian Process Distributed Port-Hamiltonian Systems
Tianyi Yang, Thomas Beckers
Gaussian process distributed port-Hamiltonian systems (GP-dPHS) enable data-driven modeling of distributed dynamical systems while preserving their physical structure. However, sta…
Embedding Physics Priors in Robot Learning: A Survey
Mattia Piccinini, Lucas Schulze, Alice Plebe +10
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remar…
Learning-Based Modeling of Soft Robots via Cosserat Rod Theory
Mohammad Ali, Nithin Senthur Kumar, Eric J. Barth +1
Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order principles is typically time-dem…
Identify then Realize: Contrastive Learning of Latent Port-Hamiltonian Dynamics from Partial Observations
Peilun Li, Kaiyuan Tan, Daniel Moyer +1
Identifying latent state representations and dynamics is essential when direct modeling in observation space is infeasible, particularly under partial and high-dimensional observat…
Data-Driven Boundary Control of Distributed Port-Hamiltonian Systems
Thomas Beckers, Leonardo Colombo
Distributed Port-Hamiltonian (dPHS) theory provides a powerful framework for modeling physical systems governed by partial differential equations and has enabled a broad class of b…
Structure-Preserving Learning of Nonholonomic Dynamics
Thomas Beckers, Anthony Bloch, Leonardo Colombo
Data-driven modeling is playing an increasing role in robotics and control, yet standard learning methods typically ignore the geometric structure of nonholonomic systems. As a con…
PHDME: Physics-Informed Diffusion Models without Explicit Governing Equations
Kaiyuan Tan, Kendra Givens, Peilun Li +1
Diffusion models provide expressive priors for forecasting trajectories of dynamical systems, but are typically unreliable in the sparse data regime. Physics-informed machine learn…