newton-usd-schemas
OpenUSD schema definitions for the Newton physics engine
Decision gist · record as of 2026-08-14
Yes, if you are authoring USD content for Newton physics simulations. The package is lightweight, actively maintained, has no dependencies, and uses a permissive license. However, note that it is in v0.x experimental status—breaking schema changes are possible in future releases, though the maintainers commit to providing migration paths. Requires Python 3.10 or later and a separate USD runtime.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; schemas must be imported before the schema registry is initialized.
- A separate USD runtime must be installed separately.
- Active project with a recent release and no runtime dependencies, making installation straightforward.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 14 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 201,532 downloads/mo, #9,669 on PyPI
Alternatives
Verify before relying
pip install newton-usd-schemas
import newton_usd_schemas # register schemas early
# Then use with your USD runtime to configure physics scenes
# and apply Newton-specific attributes to USD prims- Stability guarantees beyond 'experimental v0.x' phase—whether breaking changes are expected in near-term releases.
- Whether auto-upgrade scripts or migration tools are currently available for schema changes.
- Specific USD runtime versions or distributions known to work with this schema package.
What it is and what it does
newton-usd-schemas is a codeless schema package that extends OpenUSD's UsdPhysics specification to support Newton physics engine parameters. It contains no compiled code or public API—instead, it registers schema definitions with OpenUSD at runtime, allowing content creators to author Newton-compatible physics scenes in any USD-capable application without needing the Newton runtime installed.
The package is deployed as a pure Python wheel with zero runtime dependencies, making it lightweight to install. Once imported early in your application, it enables you to apply Newton-specific attributes to USD prims, then export those layers for later loading into a Newton runtime. It follows a minimalist design philosophy, including only attributes with clear physical meaning or cross-solver support, positioning Newton schemas as a staging ground for parameters that may eventually be promoted into the UsdPhysics standard.
Use it for
- Author physics-enabled robots and props in USD-based content creation tools without installing Newton itself.
- Configure simulation parameters in USD layers for later Newton runtime execution.
- Extend UsdPhysics with Newton-specific attributes for multi-solver physics workflows.
- Integrate Newton physics schemas into existing USD pipelines and asset libraries.
- Stage and test physics parameter definitions before proposing them for inclusion in the UsdPhysics standard.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are authoring USD content for Newton physics simulations.
The package is lightweight, actively maintained, has no dependencies, and uses a permissive license. However, note that it is in v0.x experimental status—breaking schema changes are possible in future releases, though the maintainers commit to providing migration paths. Requires Python 3.10 or later and a separate USD runtime.
Install
newton-usd-schemas on PyPI
Before you install
Active project with a recent release and no runtime dependencies, making installation straightforward. Requires Python 3.10 or later and a separate USD runtime to function.
Requires Python 3.10 or later; schemas must be imported before the schema registry is initialized. A separate USD runtime must be installed separately.
License in practice
Licensed under Apache-2.0 (permissive), allowing use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install newton-usd-schemas
import newton_usd_schemas # register schemas early
# Then use with your USD runtime to configure physics scenes
# and apply Newton-specific attributes to USD prims
Verify before relying
- Stability guarantees beyond 'experimental v0.x' phase—whether breaking changes are expected in near-term releases.
- Whether auto-upgrade scripts or migration tools are currently available for schema changes.
- Specific USD runtime versions or distributions known to work with this schema package.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 201,532 / month, #9,669 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: newton_usd_schemas-0.5.0-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 › “usd schema extensions”
- newton-usd-schemasRegisters OpenUSD schema extensions for the Newton physics engine,…
- mujoco-usd-converterConverts MuJoCo MJCF robot model files into OpenUSD layers for…
- usd-coreProvides Pixar's Universal Scene Description (USD) core libraries for…
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 mujoco-usd-converter · usd-core · urdf-usd-converter · usd-exchange · newton · newton-actuators · libpinocchio · currency-symbols · mujoco-mjx · mujoco-warp