Simulation let you test models in virtual spaces without physical prototypes. They mimic real-world physics while staying efficient.

Note that no simulation prevents the need for real-world testing. Real world properties like materials, sensor noise, human interaction, and more can’t be fully replicated. Simulations themselves can have bugs or inaccuracies that affect results.

Simulations are here to help you iterate faster, expand your mathematical toolkit, and reduce the risk of damaging hardware.

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Trade-offs when choosing a simulation backend

• Simple tools are easy to use, but don’t offer the same level of realism. They are also less extensible.
• More realistic simulations need more computing power, which can be slow or expensive. They are usually more difficult to setup and can be overkill for simple tasks.
• Open-source options are flexible and community-driven, while proprietary ones might be more polished but costly and hardware-specific.

The right choice depends on your needs and resources.

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PyBullet

PyBullet is an open-source physics engine released in 2015. It is maintained by the community, but its maintenance status is currently inactive, with no new versions released in the past year.

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Requirements

PyBullet uses URDF (Unified Robot Description Format) files to define robot structures. It’s lightweight and runs on most modern computers without specialized hardware.

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Features

PyBullet offers a balance between realism and computational efficiency, suitable for real-time simulations and educational purposes.

Despite being old, Pybullet is easy to set up, with extensive documentation and community support.

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Gazebo

Gazebo is a well-established open-source robotics simulator that has been widely used in academia and industry since its initial release in 2004. It is maintained by Open Robotics and has a strong community of contributors.

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Requirements

Gazebo uses SDF (Simulation Description Format) files to define robot models and environments. It supports a wide range of operating systems, including Linux, macOS, and Windows, and can run on standard hardware without the need for specialized equipment.

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Features

Gazebo provides a robust simulation environment with realistic physics, sensor models, and a variety of plugins for customization. It is particularly well-suited for testing robotic algorithms in complex environments.

Gazebo is known for its ease of integration with ROS, making it a popular choice for robotics research and development.

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MuJoCo

MuJoCo (Multi-Joint dynamics with Contact) is a powerful physics engine designed for simulating complex robotic systems. Initially released in 2012, it is now maintained by Google DeepMind and has become a staple in robotics research and development.

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Requirements

Required Files: MuJoCo uses XML files, specifically the MJCF (MuJoCo XML) format, to define robot models and environments. This format allows for detailed specification of the physical properties and dynamics of the simulated entities.

Hardware Requirements: MuJoCo is optimized for performance and can run efficiently on standard hardware. However, it really shines by running on GPUs and TPUs, making it suitable for data-intensive tasks like reinforcement learning.

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Features

MuJoCo is best for contact dynamics and complex interactions.

Mujoco is straightforward to set up, with comprehensive docs. The recent open-source release has made it more accessible to researchers and developers.

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NVIDIA Isaac

Release and Maintenance: NVIDIA Isaac was released in 2018 and is maintained by NVIDIA, with the latest update in January 2025.

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Requirements

Required Files: It uses USD (Universal Scene Description) files for defining environments and robots.

Hardware Requirements: NVIDIA Isaac is optimized for NVIDIA GPUs, leveraging their power for high-performance simulations.

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Features

Nvidia Isaac provides a highly realistic simulation environment. It’s great for autonomous systems.

Setting up NVIDIA Isaac can be complex, requiring specific hardware and software configurations.

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Genesis

Genesis is a physics simulation platform designed for general-purpose robotics, embodied AI, and physical AI applications. Developed by a consortium of researchers, it was released in December 2024 and is maintained by the Genesis-Embodied-AI community.

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Requirements

Genesis supports MJCF (.xml), URDF, and 3D model formats like .obj and .stl.

The framework leverages GPU-accelerated parallel computation, making it highly efficient on modern GPUs. It can run on Linux, macOS, and Windows, supporting both Nvidia and AMD GPUs.

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Features

Genesis excels in simulating a wide range of physical phenomena, including rigid body dynamics, fluid mechanics, and soft robotics. It aims to let you train AI models and test robotic systems in complex environments.

Genesis is designed to be user-friendly with a Pythonic interface, making it accessible to both beginners and experienced developers.