PyPose
- Software
- Navigation and mapping
- Library
A PyTorch library for robot learning that pairs deep perception models with physics-based optimization, offering Lie group tensors, filters, solvers and optimizers.
Made by PyPose.
Source on GitHub: github.com/pypose/pypose (opens in a new tab). Facts read at commit 9ee3610 (opens in a new tab).
Licenses
- Code
- Apache-2.0 (opens in a new tab)
Read by Robotheca from LICENSE (opens in a new tab) (Apache-2.0) at commit 9ee3610 (opens in a new tab), on 16 Oct 2026.
- Commercial use
- Yes
What it does
- Provides Lie group and Lie algebra tensors such as SO3, SE3 and Sim3 with batched operations and gradients.
- Includes system modules like LTI, LTV and NLS, plus filters such as EKF, UKF and PF.
- Offers an EPnP pose solver, an LQR controller and IMU preintegration.
- Supplies second-order optimizers such as Gauss-Newton and Levenberg-Marquardt, with schedulers to drive them.
- Adds sparse Jacobian tracing and sparse second-order optimization for accelerating bundle adjustment.
- Uses an imperative style interface aimed at easy integration into real-world robotic applications.
Written by Robotheca from the project's README (opens in a new tab) at commit 9ee3610.
Getting started
- Difficulty
- Advanced
- How to start
- The project's website (opens in a new tab)
Maintenance
- Status
- Active: a commit in the last 90 days
- Newest commit
- 16 Oct 2026 on main
- Latest release
- v0.9.5 (13 Apr 2026)
Checked on GitHub on 16 Oct 2026.
Links
Robots that can run it
No robot on Robotheca is linked to it yet. Links come from a robot's robot.yaml, from its dependency files read as text, or from Robotheca's editors.
Robotheca lists software as a reference: nothing is hosted or run here, and every fact links to where it was read.