{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"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.","skillfed_tags":["gpu-accelerated","robotics-simulation","differentiable-physics"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"newton","links":{"html":"https://skillfed.io/packages/newton","md":"https://skillfed.io/packages/newton.md","pypi":"https://pypi.org/project/newton/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"newton","python_support":"supports_current","summary":"A GPU-accelerated physics engine for robotics simulation"},"popularity":{"monthly_downloads":142588,"position":11205,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.0"}