facebookresearch/theseus

Health v2 · maintenance only

Adoption not part of the score

2055 stars · 148 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Theseus is a PyTorch-based library for building custom differentiable nonlinear optimization layers, supporting problems in robotics and vision as end-to-end differentiable architectures. It combines neural models with expert domain-specific differentiable models, differentiating through optimizers like Gauss-Newton and Levenberg-Marquardt.

Use cases

• differentiate through nonlinear least squares solvers in pytorch
• build end-to-end differentiable optimization layers for robotics
• train neural networks with optimization-based inductive priors
• solve bundle adjustment with gradients flowing to neural components
• implement bilevel optimization with implicit differentiation
• combine learned models with expert differentiable cost functions

When to choose

• you need gradients through an optimizer inside a PyTorch training loop
• you work on robotics or vision problems like SLAM, pose estimation, or bundle adjustment
• you want to embed domain priors as differentiable cost functions alongside neural networks

When to avoid

• you need a general-purpose standalone nonlinear optimizer without gradients
• your project does not use PyTorch
• you need real-time embedded optimization without a deep learning framework

Facets

library · maturity active

machine-learning deep-learning simulation math robotics computer-vision machine-learning deep-learning python cross-platform differentiable-optimization nonlinear-least-squares pytorch gauss-newton levenberg-marquardt implicit-differentiation bilevel-optimization embodied-ai factor-graphs algorithms gpu

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