Collection of differentiable methods for robotics applications implemented with Pytorch.
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Updated
Nov 1, 2023 - Python
Collection of differentiable methods for robotics applications implemented with Pytorch.
Automatic hyperparameter tuning for DeePC. Built by Michael Cummins at the Automotaic Control Laboratory, ETH Zurich.
Safe robot learning
Official code repository for ∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization (SIGGRAPH TOG 2023)
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
A library for differentiable nonlinear optimization
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