Comparative Analysis of Robotic Simulation Environments

We wanted to dive a little deeper into the role of physics in simulation and the landscape of physics engines currently popular within simulation and game engines in general. We also wanted put this in relation to the big Newton announcement this week.

Robotic simulators are crucial for developing and testing control algorithms in a virtual setting. We compare several prominent physics engines and simulation frameworks – MuJoCo, NVIDIA Isaac Lab (PhysX-based), and other relevant engines (like Bullet and Brax) – in terms of performance, ease of use, compatibility, and application scope. Each simulator has different strengths, and Nvidia Newton is designed to integrate with the best of these environments.

MuJoCo (Multi-Joint dynamics with Contact)

• Performance: MuJoCo is known for its efficient physics simulation, often running faster than real time even on a CPU
• Ease of Use: MuJoCo provides a well-designed C API and supports model descriptions in MJCF or URDF, making it relatively straightforward to define robots and environments
• Compatibility: MuJoCo is cross-platform and now open-source, which broadens its compatibility. It integrates well with reinforcement learning frameworks – for instance, the OpenAI Gym/Gymnasium and DeepMind Control Suite provide standardized MuJoCo environments for benchmarking. The MuJoCo engine itself focuses purely on physics; for rendering, it offers basic OpenGL visualization, though not photorealistic. It supports a range of robotic components (joints, actuators, sensors) needed for robotics research. With DeepMind’s open-sourcing, MuJoCo’s code can be extended or paired with other tools (e.g. JAX-based accelerators or the Warp GPU back-end).
• Application Scope: MuJoCo has been heavily used in academia and industry for robot learning, motor control, and biomechanics. Its strength lies in simulating complex contact and articulated structures (e.g. legged robots, manipulators) with good physical fidelity, which is why it became the go-to for many RL environments

NVIDIA Isaac Lab (PhysX-Based Simulation)

• Performance: Isaac Lab is built on NVIDIA’s PhysX engine and Omniverse simulation tools. PhysX is a mature, highly optimized physics engine (used in many games) that can handle large-scale simulations and has support for GPU acceleration in certain features. In Isaac Lab/Sim, PhysX is configured for robotics, including accurate joint articulation solvers and support for many agents. While PhysX on CPU is multi-threaded and quite fast, one of Isaac’s strengths is leveraging NVIDIA GPUs: for example, Isaac Sim can offload rigid body computations and use RTX GPU ray-tracing for rendering sensors, enabling high-fidelity yet still performant simulation
• Ease of Use: Compared to lightweight engines like MuJoCo, Isaac Lab/Sim can be more complex to set up – it typically requires a robust NVIDIA GPU and the installation of Isaac Sim (which is a fairly large software stack). However, NVIDIA has provided convenient tools: Isaac Lab itself is an open-source framework with a Python API that abstracts much of the low-level detail
• Compatibility: Isaac Lab is fully compatible with the NVIDIA robotics ecosystem. It uses the USD (Universal Scene Description) format to represent environments and robots, which promotes interoperability – one can import models from CAD or other simulators that support USD
• Application Scope: Isaac Lab/Sim is aimed at end-to-end robotics development. This includes robot learning (reinforcement learning and imitation learning), but also robot design, testing algorithms for navigation or manipulation, and generating synthetic data for training perception models. Its ability to simulate cameras (RGB, depth, LiDAR) with realistic visuals is a key advantage for tasks where the robot’s sensors are part of the loop (e.g., training a vision-based policy or testing an autonomous vehicle in a virtual world). Isaac has been used in applications from warehouse robot simulation to humanoid control and even digital twins of real environments. Newton’s role: Nvidia Newton will integrate with Isaac Lab as a next-gen physics option

Other Relevant Physics Engines (Bullet, ODE, Brax, etc.)

Aside from MuJoCo and Isaac/PhysX, several other physics engines are commonly used in robotics simulation, each with their own pros and cons:

• Bullet Physics (and PyBullet): Bullet is an open-source physics engine widely used in games and robotics. It features rigid body and soft body simulation, and has a convenient Python interface (PyBullet) that became popular for quick robot simulation prototypes. Bullet is appreciated for being free and fairly full-featured (collision, joints, etc.), and it has been integrated into tools like ROS/Gazebo (as an optional physics engine) and V-REP/CoppeliaSim
• ODE (Open Dynamics Engine): ODE is one of the older open-source physics engines, used historically in many robotics simulators (Gazebo’s default engine for years was ODE). It provides basic rigid body dynamics and joint physics. ODE is quite light and simple, but it struggles with simulation stability when many contacts or complex joints are involved, often requiring parameter tuning (e.g., ERP/CFM settings) to get reasonable behavior. Its performance is generally slower and less optimized compared to newer engines
• Brax (and other differentiable engines): Brax is a recent physics simulation library from Google that runs on JAX (on GPU/TPU) and is fully differentiable. It’s designed for extremely fast training of reinforcement learning agents via hardware acceleration. Brax achieves impressive speed by simplifying physics – it uses primarily spherical or capsule collisions and a penalty-based contact model, which is less accurate than constraint-based solvers but very parallelizable. The ease of use is high (Brax can be used via Python notebooks and Google Colab), and being differentiable, it’s great for research into gradient-based control. However, Brax’s simplified physics means it’s limited in scope (not suitable for fine contact dynamics or complex geometries). Other differentiable simulators (like Dojo or Tiny Differentiable Simulator) are in development as well. Newton’s relation: Newton similarly emphasizes GPU performance but aims for more generality than Brax. Newton’s differentiable physics will allow computing gradients through realistic contact dynamics, combining the benefits of Brax (speed, differentiability) with the richer physics of engines like MuJoCo
• PhysX and Others: NVIDIA’s PhysX (which we discussed via Isaac) is also used outside Isaac – for example, the Unity and Unreal game engines use PhysX or their variants for physics. Another engine, Havok, is common in industry (but not available for custom robotics use easily). DART/Drake are robotics-focused frameworks that include physics (DART uses ODE/Bullet, Drake has its own solver) and emphasize precision and robot model integration; they are powerful for planning and control research but are slower and not aimed at massive parallelism. Newton’s introduction does not directly integrate those engines, but by establishing USD as a common scene format and being open source, it opens the door for various communities to adopt Newton as an alternative backend. For instance, a robotics lab currently using Bullet or DART could switch to Newton for better performance once it matures, using the USD pipeline to transfer their models.

Newton’s Integration and Unique Advantages

Nvidia Newton is designed to seamlessly integrate with existing simulation environments like MuJoCo and Isaac Lab, effectively serving as a high-performance physics backend. By doing so, it brings a set of unique advantages to each:

• Unified Engine Across Frameworks: Newton provides a common physics engine that can run within different simulators. Developers can use MuJoCo Playground or Isaac Lab front-ends, yet rely on the same Newton physics core
• GPU-Accelerated Performance: A primary benefit of Newton is its GPU-first design. By building on NVIDIA Warp, Newton can simulate many robots or complex interactions in parallel, drastically reducing experiment time
• Differentiable and Advanced Physics: Newton offers built-in differentiable simulation, unlike standard engines. It can compute gradients of the physics outcomes with respect to input parameters or actions
• Improved Realism and Fidelity: Through collaboration and specialization, Newton is poised to improve realism. Disney’s input ensures that Newton can faithfully simulate the complex mechanisms of animatronic characters (geared linkages, compliance in joints, etc.), which in turn benefits anyone simulating robots with similar complexity. Google DeepMind’s influence ensures Newton maintains scientific rigor (e.g., proper dynamic equations, no arbitrary hacks that learning agents could exploit
• Scalability and Community-Driven Development: As an open-source project, Newton invites contributions from the robotics community, much like OpenAI’s Gym or DeepMind’s open-sourced MuJoCo have communities around them. This means the engine can evolve quickly with new features, bug fixes, and performance improvements contributed by users. Its open nature and modular design make it easier to maintain in the long run and adapt to new hardware (future GPUs, etc.) or new research needs. For organizations using different simulators today, Newton offers a path to consolidate efforts: instead of each lab writing their own physics tweaks or each simulator having a separate physics core, Newton can become a standardized backbone. This convergence can lead to better validated physics (since many will test and improve it) and a richer set of tools (common file formats, viewers, analysis scripts that work with Newton outputs, etc.). NVIDIA, DeepMind, and Disney are providing the initial momentum, but the intent is a sustainable, community-driven engine

Nvidia Newton represents a significant step forward in robotics simulation. Each partner organization has infused the project with unique strengths – NVIDIA with GPU prowess and integration, DeepMind with fast and accurate contact physics, Disney with demanding real-world robot applications – resulting in an engine poised to serve a wide range of needs. When comparing simulation environments, Newton doesn’t replace MuJoCo or Isaac; instead, it enhances and unifies them. MuJoCo gains unprecedented speed and gradients, Isaac Lab gains openness and advanced physics, and the broader field gains a platform that marries realism with performance. As Newton becomes available (the first version is expected sometime in 2025), we can expect the gap between simulation and reality to narrow, accelerating the development of robots from animated characters in theme parks to general-purpose humanoids in industry.