Newton 1.0 GA Release Announcement & Roadmap

[momo-van]

Newton 1.0 Release Highlights

Newton 1.0 is now generally available on PyPI. Here are some of the key highlights from this release.

Stable API: Newton API provides a stable, unified interface for a wide range of capabilities in modeling, solving, controlling, and sensing in the simulation.

MuJoCo Warp builds on the stability and accuracy the robotics community already trusts in MuJoCo, developed by Google DeepMind, is a generalized coordinate solver which is now extended with GPU-scale throughput for thousands of parallel training environments. New optimizations enable MuJoCo Warp to speed up MJX by 252x for locomotion, and 475x for manipulation tasks on the NVIDIA RTX PRO 6000 Blackwell Series.

Kamino, developed by Disney Research, is a maximal coordinate solver handling complex mechanisms such as robotic hands and legged systems with closed-loop linkages and remote actuation. It enables a new class of simulation capabilities, giving mechanical designers the freedom to design systems without worrying about simulatability, while paving the way for scalable reinforcement learning. Kamino is in beta mode.

Deformable solvers: Powered by the Vertex Block Descent (VBD) solver, Newton handles linear deformables (cables), thin deformables (cloth), and volumetric deformables (rubber parts), covering common materials found in real industrial settings. Also, the Implicit Material Point Method (MPM) handles particle simulation (granular material) applicable to rough terrain locomotion scenarios. The VBD and MPM solvers can be coupled with MuJoCo Warp explicitly to support deformable manipulation and locomotion scenarios with robotic systems.

Collision library: A flexible and fast collision detection pipeline enables selection of the right broadphase and narrowphase detection approaches based on scene complexity. The pipeline is reusable and can accelerate custom solver development. The library includes advanced contact generation and modeling

• Signed distance field (SDF)-based collision captures complex geometries directly from CAD-exported meshes, eliminating the need for mesh approximation methods. This is critical for tight-tolerance tasks such as connector insertion or in-hand manipulation.

• Hydroelastic contacts, inspired by this Drake contact model, use a continuous pressure distribution across finite-area contact patches rather than a set of contact points. This provides higher-fidelity and more robust object interaction required for tactile sensing and manipulation policies, ultimately achieving better sim-to-real transfer.

OpenUSD and Isaac integration: With OpenUSD as a common data layer, Newton integrates natively with NVIDIA Isaac Sim 6.0 and Isaac Lab 3.0 early access releases, enabling faster workflows from robot description to trained policy and evaluation pipelines across reinforcement and imitation learning workflows.

Tiled camera sensor: A Warp-based tiled camera sensor supports high-throughput simplified rendering with channels for RGB, depth, albedo, surface normals, and instance segmentation. Designed to scale vision-based RL policies, it enables end-to-end perceptive training pipelines to run on the NVIDIA DGX platform. The rendering backend is ray-tracing-based and supports multiple scene representations, including triangle meshes and Gaussian splats.

Read more about this release and some of our recent industrial use-cases in this technical blog. Newton Adds Contact-Rich Manipulation and Locomotion Capabilities for Industrial Robotics | NVIDIA Technical Blog

Roadmap

Going forward, Newton will follow a monthly release cadence, with features rolled out incrementally. Key roadmap items the team is actively working on are outlined below.

In addition to these, we are also exploring support for low-latency applications, faster CPU execution, deterministic simulation, and broader differentiability coverage.

We welcome community feedback of all kinds, from user experience and feature set to performance improvements and defect reports. Please join us, and the broader community, on GitHub Discussions and Issues. We look forward to seeing what you build with Newton and to hearing your feedback.