no code implementations • 12 May 2024 • Max Yang, Chenghua Lu, Alex Church, Yijiong Lin, Chris Ford, Haoran Li, Efi Psomopoulou, David A. W. Barton, Nathan F. Lepora
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.
1 code implementation • 3 May 2024 • Roussel Desmond Nzoyem, David A. W. Barton, Tom Deakin
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.
1 code implementation • 2 Oct 2023 • Roussel Desmond Nzoyem, David A. W. Barton, Tom Deakin
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.
no code implementations • 22 Oct 2021 • Sandor Beregi, David A. W. Barton, Djamel Rezgui, Simon A. Neild
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.