pypose/pypose

Health v2 · maintenance only

Adoption not part of the score

1605 stars · 131 forks observed · 2026-08-28

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

PyPose is a PyTorch-based Python library for differentiable robotics on manifolds, combining deep perceptual models with physics-based optimization. It provides Lie group/algebra tensors (SO3, SE3, Sim3, RxSO3), robotics modules like EKF/UKF filters, IMU preintegration, LQR, and PnP solvers, plus second-order optimizers such as Gauss-Newton and Levenberg-Marquardt.

Use cases

• differentiable robotics research with pytorch
• SLAM and visual-inertial odometry development
• pose graph optimization and bundle adjustment
• IMU preintegration for state estimation
• Kalman filtering for robot localization
• robot control and MPC with learned components
• differentiable Lie group operations on SE(3)
• Gauss-Newton and Levenberg-Marquardt optimization in deep learning pipelines

When to choose

• you need differentiable geometric robotics primitives integrated with PyTorch
• you are building SLAM, VIO, control, or planning systems that mix learning and physics-based optimization
• you need efficient, parallelized Jacobians for Lie groups and Lie algebras
• you want second-order optimizers for factor-graph or bundle-adjustment style problems

When to avoid

• you need a full out-of-the-box SLAM pipeline rather than building blocks
• your project does not use PyTorch or Python
• you need real-time embedded deployment without GPU/autodiff overhead
• you only need classical robotics libraries without differentiability

Facets

library · maturity active

machine-learning math simulation robotics machine-learning autonomous-vehicles simulation computer-vision python windows robotics pytorch lie-groups differentiable-optimization slam kalman-filter pose-estimation pose-graph-optimization imu-preintegration control motion-planning gauss-newton levenberg-marquardt state-estimation linux macos gpu

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