Pypose

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pypose is an open-source library for differentiable robotics on manifolds, built on PyTorch. It combines deep perceptual models with physics-based optimization for robot learning.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is pypose?

pypose is an open-source, PyTorch-based library designed for differentiable robotics on manifolds. It facilitates robot learning by integrating deep perceptual models with physics-based optimization techniques. The library offers a user-friendly, efficient, and interpretable architecture, making it suitable for real-world robotic applications. Key features include LieTensor for Lie groups and algebras (SO3, SE3, Sim3, RxSO3), various modules for system modeling (LTI, LTV, NLS), filtering (EKF, UKF, PF), and PnP solvers. It also provides second-order optimizers like GaussNewton and LevenbergMarquardt, with high efficiency and parallel computing support for Jacobian operations.

Best used for

Ideal for developers and data scientists who need to build advanced robotic perception systems, optimize robot learning with physics-based methods, and perform complex pose estimation. Especially valuable for researchers and engineers working on SLAM, control, and geometric deep learning applications.

Common actions

develop robotics applications

optimize robot learning

implement physics models

perform pose estimation

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Capabilities

Key features

Differentiable robotics
Lie group operations
Physics-based optimization
System modeling modules
Second-order optimizers
IMU preintegration

Target Audience

developerdata scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the core components of PyPose for robotics?

PyPose includes LieTensor for Lie groups (SO3, SE3, Sim3, RxSO3) and Lie algebras, along with modules for Linear Time-Invariant (LTI) and Linear Time-Varying (LTV) systems, Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), Particle Filters (PF), and PnP solvers.

How does PyPose enhance efficiency for batched operations?

PyPose is designed for high efficiency, particularly supporting parallel computing for the Jacobian of Lie group and Lie algebra operations. This allows for faster processing of batched operations compared to other libraries, as detailed in its research paper.

Can PyPose be used with second-order optimizers?

Yes, PyPose integrates second-order optimizers such as Gauss-Newton and Levenberg-Marquardt. These can be used for tasks like estimating batched inverse transforms, providing robust optimization capabilities for various robotic problems.

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