How do I choose between different physics engines for robotics and simulation platforms?

Choosing a physics engine for robotics and simulation depends on what you care about most: accuracy, speed, contact quality, sensor support, ease of integration, and community/tooling. There isn’t one best engine for every case.

1) Start with your use case

Ask:

• Do you need real-time simulation?
  • For control, RL, and HIL-style workflows, speed matters a lot.
• Do you need accurate contact and grasping?
  • If yes, contact stability and friction modeling are critical.
• Is your robot mostly rigid-body?
  • Most common robotics use cases are.
• Do you need soft bodies, fluids, or deformables?
  • Many engines have limited support here.
• Do you need sensor simulation?
  • Cameras, LiDAR, IMUs, depth, and ray casting support may be as important as dynamics.
• Do you need large-scale batch simulation?
  • For RL, parallelism and headless execution are important.
• Do you need strong ROS integration or existing robotics workflows?

2) Key criteria to compare

Dynamics realism

• Good joint behavior
• Stable constraint solving
• Contact/friction consistency
• Timestep robustness

Performance

• Sim speed for single environments
• Parallel throughput
• GPU acceleration support
• Headless mode and batch execution

Contact handling

• Box stacking, grasping, peg-in-hole, foot-ground interaction
• Known issues with jitter, penetration, or sticking/sliding artifacts

Determinism

• Important for reproducibility and debugging
• Some engines are less deterministic across hardware or threads

API and ecosystem

• Python support
• URDF/SDF/MJCF import
• ROS / ROS2 compatibility
• Integration with visualization tools

Sensors and rendering

• Camera realism, depth noise, ray casting, segmentation
• Visual fidelity if you care about perception

Licensing and maintenance

• Open source vs commercial
• How active the project is
• Community size and documentation quality

3) Common choices and typical strengths

MuJoCo

Best for:

• Robot control
• Reinforcement learning
• Fast, stable rigid-body simulation
• Good contact handling for many robotics tasks

Strengths:

• Very fast
• Excellent for articulated robots
• Widely used in research

Limitations:

• Not aimed at photorealistic rendering
• Some complex contact scenarios still approximate

PyBullet / Bullet

Best for:

• Quick prototyping
• Open-source workflows
• Broad accessibility

Strengths:

• Easy to get started
• Widely used
• Good enough for many robotics tasks

Limitations:

• Contact realism and stability can be less robust than MuJoCo in some cases
• Performance and accuracy trade-offs vary

NVIDIA Isaac Sim / PhysX

Best for:

• GPU-accelerated robotics simulation
• Synthetic data generation
• Sensor-rich simulation
• Large-scale parallelization

Strengths:

• Strong visual/sensor simulation
• Good tooling for robotics and perception
• Scales well for some workflows

Limitations:

• Heavier setup
• Requires NVIDIA ecosystem
• Can be more complex than lighter engines

Gazebo / Ignition / Fortress

Best for:

• ROS-centric robotics development
• System integration
• Multi-robot simulation

Strengths:

• Strong robotics ecosystem
• Good for integration/testing
• Common in ROS workflows

Limitations:

• Performance and contact realism depend on backend and configuration
• Can be heavier and less uniform in user experience

Webots

Best for:

• Education
• Prototyping
• Simpler robot scenarios

Strengths:

• Easy to use
• Good for teaching and moderate robotics tasks

Limitations:

• Less suited to high-end performance or large-scale RL

4) A practical decision framework

Choose MuJoCo if:

• You need reliable rigid-body robotics simulation
• You care about control and contact stability
• You want strong performance with relatively simple setup

Choose Isaac Sim / PhysX if:

• You need realistic sensors, visuals, or synthetic data
• You want GPU acceleration
• You’re comfortable with NVIDIA tooling

Choose Gazebo if:

• You work heavily with ROS/ROS2
• You need integration testing with robot stacks
• You care more about robotics workflows than raw physics performance

Choose PyBullet if:

• You want open-source simplicity
• You need fast experimentation
• You can tolerate some limitations in contact fidelity

Choose Webots if:

• You want an approachable environment
• You’re doing education or moderate robotics testing

5) Test engines with your own benchmark

The best way to decide is to run a small benchmark on your actual task:

• A robot standing, walking, or manipulating
• A grasping task with your gripper
• A trajectory tracking task
• A sensor pipeline if perception matters

Measure:

• Success rate
• Stability over long rollouts
• Wall-clock speed
• Reproducibility
• Setup and debugging time

6) Rule of thumb

• Control and RL: MuJoCo
• ROS integration: Gazebo
• Sensors + visuals + synthetic data: Isaac Sim
• Simple open-source prototyping: PyBullet
• Teaching and accessibility: Webots

If you want, I can also give you:

1. a comparison table of major engines, or
2. a decision tree based on your exact robotics project.