yourdfpy
A simpler and easier-to-use library for loading, manipulating, saving, and visualizing URDF files.
Decision gist · record as of 2026-08-14
Yes, if you work with URDF files and need robust parsing. The library's decoupled design and superior robustness on real-world models (12/12 success vs. competitors' 4–6/12) make it a practical choice over alternatives. Install friction is low and maintenance is active. MIT licensing removes legal friction. No known vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.7 or later; mesh visualization requires a display or headless rendering setup.
- Low install friction with a pure-Python wheel.
- Actively maintained with recent commits and a stable release cadence; marked as Beta but in active development.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-01-23 (203 days) · last repo commit 2026-05-10 · 293 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,940,324 downloads/mo, #3,418 on PyPI
Alternatives
Verify before relying
pip install yourdfpy
import yourdfpy
robot = yourdfpy.URDF.load('robot.urdf')
scene = robot.scene # Access visualization scene- Whether forward kinematics calculations are fully implemented or partial
- Extent of scene-graph building capabilities and performance characteristics
- Support for URDF extensions beyond the standard specification
What it is and what it does
Yourdfpy is a URDF (Unified Robot Description Format) parser designed to handle real-world robot model files more robustly than existing alternatives. It decouples parsing from validation and mesh loading, allowing you to load and inspect URDF files independently of whether you validate them or load their associated mesh geometry. The library depends on lxml for XML parsing, numpy for numerical operations, trimesh for mesh handling, and six for Python 2/3 compatibility.
The package is intended for roboticists, simulation engineers, and developers working with robot models. It provides both programmatic access to URDF structure and a command-line visualization tool with interactive keyboard controls for inspecting models, toggling axis markers, wireframe rendering, and back-face culling. The fact sheet shows it successfully loads URDF files that other parsers fail on, making it useful when working with diverse or non-standard robot descriptions.
Use it for
- Load and inspect URDF files from diverse robot platforms without parser failures
- Visualize robot models interactively from the command line with axis and wireframe toggles
- Extract and manipulate robot structure (joints, links, frames) for kinematic analysis
- Batch-process multiple URDF files with optional mesh loading for performance control
- Validate URDF syntax and structure independently of mesh availability
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with URDF files and need robust parsing.
The library's decoupled design and superior robustness on real-world models (12/12 success vs. competitors' 4–6/12) make it a practical choice over alternatives. Install friction is low and maintenance is active. MIT licensing removes legal friction. No known vulnerabilities.
Install
yourdfpy on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained with recent commits and a stable release cadence; marked as Beta but in active development.
Requires Python 3.7 or later; mesh visualization requires a display or headless rendering setup.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install yourdfpy
import yourdfpy
robot = yourdfpy.URDF.load('robot.urdf')
scene = robot.scene # Access visualization scene
Verify before relying
- Whether forward kinematics calculations are fully implemented or partial
- Extent of scene-graph building capabilities and performance characteristics
- Support for URDF extensions beyond the standard specification
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesimportlib-metadatalxmltrimeshnumpysix |
| Maintenance | Actively maintained 203 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,940,324 / month, #3,418 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8 |
Evidence: yourdfpy-0.0.60-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “urdf parser python”
- yourdfpyLoads, manipulates, validates, and visualizes URDF robot description…
- cmeel-urdfdomParses URDF (U-Robot Description Format) XML files into Python data…
- urdf-usd-converterConverts URDF robot description files into OpenUSD format, preserving…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also robot_descriptions · cmeel-urdfdom · urdf-usd-converter · pybullet · resolve-robotics-uri-py · robotspy · flexivrdk · mplib · robotframework-excellib