GPU Acceleration on Producthunt daily

newton

Thu, 19 Mar 2026 15:53:02 +0800

newton-physics/newton

Newton

Newton is a GPU-accelerated physics simulation engine built upon NVIDIA Warp, specifically targeting roboticists and simulation researchers.

Newton extends and generalizes Warp’s (deprecated) warp.sim module, and integrates MuJoCo Warp as its primary backend. Newton emphasizes GPU-based computation, OpenUSD support, differentiability, and user-defined extensibility, facilitating rapid iteration and scalable robotics simulation.

Newton is a Linux Foundation project that is community-built and maintained. Code is licensed under Apache-2.0. Documentation is licensed under CC-BY-4.0.

Newton was initiated by Disney Research, Google DeepMind, and NVIDIA.

Requirements

Python 3.10+
OS: Linux (x86-64, aarch64), Windows (x86-64), or macOS (CPU only)
GPU: NVIDIA GPU (Maxwell or newer), driver 545 or newer (CUDA 12). No local CUDA Toolkit installation required. macOS runs on CPU.

For detailed system requirements and tested configurations, see the installation guide.

Quickstart

1
2

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

To install from source with uv, see the installation guide.

Examples

Before running the examples below, install Newton with the examples extra:

`1`	`pip install "newton[examples]"`

If you installed from source with uv, substitute uv run for python in the commands below.

Basic Examples

`python -m newton.examples basic_pendulum`	`python -m newton.examples basic_urdf`	`python -m newton.examples basic_viewer`

`python -m newton.examples basic_shapes`	`python -m newton.examples basic_joints`	`python -m newton.examples basic_conveyor`

`python -m newton.examples basic_heightfield`	`python -m newton.examples recording`	`python -m newton.examples replay_viewer`

`python -m newton.examples basic_plotting`
Robot Examples

`python -m newton.examples robot_cartpole`	`python -m newton.examples robot_g1`	`python -m newton.examples robot_h1`

`python -m newton.examples robot_anymal_d`	`python -m newton.examples robot_anymal_c_walk`

`python -m newton.examples robot_policy`	`python -m newton.examples robot_ur10`	`python -m newton.examples robot_panda_hydro`

`python -m newton.examples robot_allegro_hand`
Cable Examples

`python -m newton.examples cable_twist`	`python -m newton.examples cable_y_junction`	`python -m newton.examples cable_bundle_hysteresis`

`python -m newton.examples cable_pile`
Cloth Examples

`python -m newton.examples cloth_bending`	`python -m newton.examples cloth_hanging`	`python -m newton.examples cloth_style3d`

`python -m newton.examples cloth_h1`	`python -m newton.examples cloth_twist`	`python -m newton.examples cloth_franka`

`python -m newton.examples cloth_rollers`	`python -m newton.examples cloth_poker_cards`
Inverse Kinematics Examples

`python -m newton.examples ik_franka`	`python -m newton.examples ik_h1`	`python -m newton.examples ik_custom`

`python -m newton.examples ik_cube_stacking`
MPM Examples

`python -m newton.examples mpm_granular`	`python -m newton.examples mpm_anymal`	`python -m newton.examples mpm_twoway_coupling`

`python -m newton.examples mpm_grain_rendering`	`python -m newton.examples mpm_multi_material`
Sensor Examples

`python -m newton.examples sensor_contact`	`python -m newton.examples sensor_tiled_camera`	`python -m newton.examples sensor_imu`
Selection Examples

`python -m newton.examples selection_cartpole`	`python -m newton.examples selection_materials`	`python -m newton.examples selection_articulations`

`python -m newton.examples selection_multiple`
DiffSim Examples

`python -m newton.examples diffsim_ball`	`python -m newton.examples diffsim_cloth`	`python -m newton.examples diffsim_drone`

`python -m newton.examples diffsim_spring_cage`	`python -m newton.examples diffsim_soft_body`	`python -m newton.examples diffsim_bear`
Multi-Physics Examples

`python -m newton.examples softbody_gift`	`python -m newton.examples softbody_dropping_to_cloth`
Contacts Examples

`python -m newton.examples nut_bolt_hydro`	`python -m newton.examples nut_bolt_sdf`	`python -m newton.examples brick_stacking`

`python -m newton.examples pyramid`
Softbody Examples

`python -m newton.examples softbody_hanging`	`python -m newton.examples softbody_franka`

Example Options

The examples support the following command-line arguments:

Argument	Description	Default
`--viewer`	Viewer type: `gl` (OpenGL window), `usd` (USD file output), `rerun` (ReRun), or `null` (no viewer).	`gl`
`--device`	Compute device to use, e.g., `cpu`, `cuda:0`, etc.	`None` (default Warp device)
`--num-frames`	Number of frames to simulate (for USD output).	`100`
`--output-path`	Output path for USD files (required if `--viewer usd` is used).	`None`

Some examples may add additional arguments (see their respective source files for details).

Example Usage

# List available examples
python -m newton.examples

# Run with the USD viewer and save to my_output.usd
python -m newton.examples basic_viewer --viewer usd --output-path my_output.usd

# Run on a selected device
python -m newton.examples basic_urdf --device cuda:0

# Combine options
python -m newton.examples basic_viewer --viewer gl --num-frames 500 --device cpu

Contributing and Development

See the contribution guidelines and the development guide for instructions on how to contribute to Newton.

Support and Community Discussion

For questions, please consult the Newton documentation first before creating a discussion in the main repository.

Code of Conduct

By participating in this community, you agree to abide by the Linux Foundation Code of Conduct.

Project Governance, Legal, and Members

Please see the newton-governance repository for more information about project governance.

IsaacLab

Fri, 04 Jul 2025 15:30:49 +0800

isaac-sim/IsaacLab

Isaac Lab

Isaac Lab is a GPU-accelerated, open-source framework designed to unify and simplify robotics research workflows, such as reinforcement learning, imitation learning, and motion planning. Built on NVIDIA Isaac Sim, it combines fast and accurate physics and sensor simulation, making it an ideal choice for sim-to-real transfer in robotics.

Isaac Lab provides developers with a range of essential features for accurate sensor simulation, such as RTX-based cameras, LIDAR, or contact sensors. The framework’s GPU acceleration enables users to run complex simulations and computations faster, which is key for iterative processes like reinforcement learning and data-intensive tasks. Moreover, Isaac Lab can run locally or be distributed across the cloud, offering flexibility for large-scale deployments.

Key Features

Isaac Lab offers a comprehensive set of tools and environments designed to facilitate robot learning:

Robots: A diverse collection of robots, from manipulators, quadrupeds, to humanoids, with 16 commonly available models.
Environments: Ready-to-train implementations of more than 30 environments, which can be trained with popular reinforcement learning frameworks such as RSL RL, SKRL, RL Games, or Stable Baselines. We also support multi-agent reinforcement learning.
Physics: Rigid bodies, articulated systems, deformable objects
Sensors: RGB/depth/segmentation cameras, camera annotations, IMU, contact sensors, ray casters.

Getting Started

Our documentation page provides everything you need to get started, including detailed tutorials and step-by-step guides. Follow these links to learn more about:

Isaac Sim Version Dependency

Isaac Lab is built on top of Isaac Sim and requires specific versions of Isaac Sim that are compatible with each release of Isaac Lab. Below, we outline the recent Isaac Lab releases and GitHub branches and their corresponding dependency versions for Isaac Sim.

Isaac Lab Version	Isaac Sim Version
`main` branch	Isaac Sim 4.5
`v2.1.0`	Isaac Sim 4.5
`v2.0.2`	Isaac Sim 4.5
`v2.0.1`	Isaac Sim 4.5
`v2.0.0`	Isaac Sim 4.5
`feature/isaacsim_5_0` branch	Isaac Sim 5.0

Note that the feature/isaacsim_5_0 will contain active updates and may contain some breaking changes until the official Isaac Lab 2.2 release. It currently requires the Isaac Sim 5.0 branch available on GitHub built from source. Please refer to the README in the feature/isaacsim_5_0 branch for instructions for using Isaac Lab with Isaac Sim 5.0. We are actively working on introducing backwards compatibility support for Isaac Sim 4.5 for this branch.

Contributing to Isaac Lab

We wholeheartedly welcome contributions from the community to make this framework mature and useful for everyone. These may happen as bug reports, feature requests, or code contributions. For details, please check our contribution guidelines.

We encourage you to utilize our Show & Tell area in the Discussions section of this repository. This space is designed for you to:

Share the tutorials you’ve created
Showcase your learning content
Present exciting projects you’ve developed

By sharing your work, you’ll inspire others and contribute to the collective knowledge of our community. Your contributions can spark new ideas and collaborations, fostering innovation in robotics and simulation.

Troubleshooting

Please see the troubleshooting section for common fixes or submit an issue.

For issues related to Isaac Sim, we recommend checking its documentation or opening a question on its forums.

Support

Please use GitHub Discussions for discussing ideas, asking questions, and requests for new features.
Github Issues should only be used to track executable pieces of work with a definite scope and a clear deliverable. These can be fixing bugs, documentation issues, new features, or general updates.

Connect with the NVIDIA Omniverse Community

Do you have a project or resource you’d like to share more widely? We’d love to hear from you! Reach out to the NVIDIA Omniverse Community team at OmniverseCommunity@nvidia.com to explore opportunities to spotlight your work.

You can also join the conversation on the Omniverse Discord to connect with other developers, share your projects, and help grow a vibrant, collaborative ecosystem where creativity and technology intersect. Your contributions can make a meaningful impact on the Isaac Lab community and beyond!

License

The Isaac Lab framework is released under BSD-3 License. The isaaclab_mimic extension and its corresponding standalone scripts are released under Apache 2.0. The license files of its dependencies and assets are present in the docs/licenses directory.

Acknowledgement

Isaac Lab development initiated from the Orbit framework. We would appreciate if you would cite it in academic publications as well:

@article{mittal2023orbit,
   author={Mittal, Mayank and Yu, Calvin and Yu, Qinxi and Liu, Jingzhou and Rudin, Nikita and Hoeller, David and Yuan, Jia Lin and Singh, Ritvik and Guo, Yunrong and Mazhar, Hammad and Mandlekar, Ajay and Babich, Buck and State, Gavriel and Hutter, Marco and Garg, Animesh},
   journal={IEEE Robotics and Automation Letters},
   title={Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments},
   year={2023},
   volume={8},
   number={6},
   pages={3740-3747},
   doi={10.1109/LRA.2023.3270034}
}

GPU Acceleration on Producthunt daily

newton

newton-physics/newton

Newton

Requirements

Quickstart

Examples

Basic Examples

Robot Examples

Cable Examples

Cloth Examples

Inverse Kinematics Examples

MPM Examples

Sensor Examples

Selection Examples

DiffSim Examples

Multi-Physics Examples

Contacts Examples

Softbody Examples