Imperative learning: A self-supervised neural-symbolic learning framework for robot autonomy
Data-driven methods such as reinforcement and imitation learning have achieved
remarkable success in robot autonomy. However, their data-centric nature still hinders them …
remarkable success in robot autonomy. However, their data-centric nature still hinders them …
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 …
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks
A continuous-time dynamical system with parameter ε is nearly-periodic if all its trajectories
are periodic with nowhere-vanishing angular frequency as ε approaches 0. Nearly-periodic …
are periodic with nowhere-vanishing angular frequency as ε approaches 0. Nearly-periodic …
Hamiltonian dynamics learning from point cloud observations for nonholonomic mobile robot control
A Altawaitan, J Stanley, S Ghosal… - … on Robotics and …, 2024 - ieeexplore.ieee.org
Reliable autonomous navigation requires adapting the control policy of a mobile robot in
response to dynamics changes in different operational conditions. Hand-designed dynamics …
response to dynamics changes in different operational conditions. Hand-designed dynamics …
Geometry preserving Ito-Taylor formulation for stochastic hamiltonian dynamics on manifolds
Naturally occurring systems often have inherent uncertainties and often evolve on
complicated smooth and differentiable hypersurfaces that are not necessarily Euclidean …
complicated smooth and differentiable hypersurfaces that are not necessarily Euclidean …
Practical perspectives on symplectic accelerated optimization
V Duruisseaux, M Leok - Optimization Methods and Software, 2023 - Taylor & Francis
Geometric numerical integration has recently been exploited to design symplectic
accelerated optimization algorithms by simulating the Bregman Lagrangian and Hamiltonian …
accelerated optimization algorithms by simulating the Bregman Lagrangian and Hamiltonian …
Towards enforcing hard physics constraints in operator learning frameworks
Enforcing physics constraints in surrogate models for PDE evolution operators can improve
the physics plausibility of their predictions and their convergence and generalization …
the physics plausibility of their predictions and their convergence and generalization …
Projected Neural Differential Equations for Learning Constrained Dynamics
Neural differential equations offer a powerful approach for learning dynamics from data.
However, they do not impose known constraints that should be obeyed by the learned …
However, they do not impose known constraints that should be obeyed by the learned …
PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving
Motion prediction is critical for autonomous off-road driving, however, it presents significantly
more challenges than on-road driving because of the complex interaction between the …
more challenges than on-road driving because of the complex interaction between the …
The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics
Optimization is a universal quest, reflecting the basic human need to do better. Improved
optimizations of energy‐efficiency, safety, robustness, and other criteria in engineered …
optimizations of energy‐efficiency, safety, robustness, and other criteria in engineered …