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.
Preliminary code for the paper "Learning Deterministic Surrogates for Robust Convex QCQPs".
Automatic hyperparameter tuning for DeePC. Built by Michael Cummins at the Automotaic Control Laboratory, ETH Zurich.
Tutorial on Deep Declarative Networks
Implementation and examples from Trajectory Optimization with Optimization-Based Dynamics https://arxiv.org/abs/2109.04928
Official code repository for ∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization (SIGGRAPH TOG 2023)
Official repo for the paper "SAGE: SLAM with Appearance and Geometry Prior for Endoscopy" (ICRA 2022)
Safe robot learning
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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