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Found 4 papers, 2 papers with code
AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch
In the real world, we demonstrate successful sim-to-real transfer of the dense tactile policy, generalizing to a diverse range of objects for various rotation axes and hand directions and outperforming other forms of low-dimensional touch.
Neural Context Flows for Learning Generalizable Dynamical Systems
Neural Ordinary Differential Equations typically struggle to generalize to new dynamical behaviors created by parameter changes in the underlying system, even when the dynamics are close to previously seen behaviors.
A Comparison of Mesh-Free Differentiable Programming and Data-Driven Strategies for Optimal Control under PDE Constraints
The field of Optimal Control under Partial Differential Equations (PDE) constraints is rapidly changing under the influence of Deep Learning and the accompanying automatic differentiation libraries.
Using scientific machine learning for experimental bifurcation analysis of dynamic systems
Augmenting mechanistic ordinary differential equation (ODE) models with machine-learnable structures is an novel approach to create highly accurate, low-dimensional models of engineering systems incorporating both expert knowledge and reality through measurement data.
