---
id: newton
version: "1.5.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# newton — A GPU-accelerated physics engine for robotics simulation

License: permissive · Maintenance: active · Downloads: 142.6K/mo

## What it is and what it does

Newton is a GPU-accelerated physics simulation engine purpose-built for robotics and simulation research. It extends NVIDIA Warp's simulation capabilities and integrates MuJoCo Warp as its primary backend, emphasizing GPU-based computation, differentiability, and extensibility. The engine supports a wide range of simulation scenarios including rigid bodies, soft bodies, cloth, cables, and material point method (MPM) simulations, with built-in support for OpenUSD and multiple visualization backends.

The package is designed for rapid iteration in robotics research, offering examples for robot control, inverse kinematics, contact dynamics, and multi-physics coupling. It requires Python 3.10+, an NVIDIA GPU with Maxwell architecture or newer (driver 545+), or can run CPU-only on macOS. Installation is straightforward via pip, with optional examples available. The project is community-maintained under Linux Foundation stewardship and was initiated by Disney Research, Google DeepMind, and NVIDIA.

Use it for:

- Simulate robot dynamics and control policies for humanoid and legged robots like Franka, H1, and ANYmal.
- Develop and test inverse kinematics solutions with GPU acceleration for real-time performance.
- Model cloth and cable interactions in manipulation tasks, such as pick-and-place with deformable objects.
- Run differentiable simulations for gradient-based optimization of robot morphologies or control parameters.
- Prototype multi-physics scenarios combining rigid bodies, soft bodies, and granular materials in a single simulation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Newton is a GPU-accelerated physics simulation engine built on NVIDIA Warp and MuJoCo Warp, designed for robotics and simulation research with support for rigid bodies, soft bodies, cloth, cables, and differentiable simulation.

Yes, if you are a roboticist or simulation researcher with access to an NVIDIA GPU and need GPU-accelerated physics with differentiability and extensibility. The low install friction, active maintenance, permissive license, and comprehensive example suite make it a solid choice. No security vulnerabilities reported. Consider if you require CPU-only simulation on non-macOS platforms or need physics engines not covered by the provided examples.

## Install

pip install newton
uv add newton
poetry add newton

## Installing newton

Before you install: Low friction installation via pip; requires Python 3.10+, NVIDIA GPU with driver 545 or newer (CUDA 12), or CPU-only on macOS. Active maintenance with release 3 days old.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions; documentation separately licensed CC-BY-4.0.

Quickstart:

pip install "newton[examples]"
python -m newton.examples basic_pendulum

Requires Python 3.10+; NVIDIA GPU with Maxwell architecture or newer and driver 545+, or runs CPU-only on macOS.

Verify before relying:

- Whether warp-lang dependency is automatically installed or requires separate setup.
- Performance characteristics and simulation scale limits compared to other physics engines.
- Availability and maturity of the OpenUSD integration mentioned in the description.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 142.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

GPU physics simulation, robotics simulation engine, differentiable physics, NVIDIA Warp physics, rigid body dynamics GPU, cloth and soft body simulation, inverse kinematics solver, gpu-accelerated, robotics-simulation, differentiable-physics

[View on SkillFed](https://skillfed.io/packages/newton) · [View on PyPI](https://pypi.org/project/newton/)