1 citations · 1 across the 9 of their papers we have counts for
10 papers
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…
Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models
Kaiyuan Tan, Peilun Li, Jun Wang +1
The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state p…
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models
Kaiyuan Tan, Peilun Li, Thomas Beckers
Modeling the dynamics of flexible objects has become an emerging topic in the community as these objects become more present in many applications, e.g., soft robotics. Due to the p…
Data-driven Bayesian Control of Port-Hamiltonian Systems
Thomas Beckers
Port-Hamiltonian theory is an established way to describe nonlinear physical systems widely used in various fields such as robotics, energy management, and mechanical engineering.…