Robot Simulation 101 — how physics engines simulate robots

What actually happens inside a robot simulator: rigid-body dynamics, integrators, contact and friction solvers, joint drives, sensor models, and controllers — plus why simulations explode and how the reality gap arises.

By Rahul Rajelli · updated 2026-07-10 · RSS

A robot simulator is a program that repeatedly answers one question: given where every rigid body is and what forces act on it, where will everything be a millisecond from now? Everything else — pretty rendering, sensor feeds, ROS integration — is built on that loop. Understanding the loop is what separates "I ran the tutorial" from "I know why my robot just launched into orbit."

The simulation loop

Every physics engine (Gazebo's physics backends, MuJoCo, PhysX inside Isaac Sim, Bullet) runs the same cycle, typically 1,000–10,000 times per simulated second:

1. Collision detection — which bodies are touching or about to touch?
2. Force assembly — gravity, joint actuator torques, springs/dampers, external pushes.
3. Constraint solving — joints must stay connected, contacts must not interpenetrate, friction must obey its cone. This is a big simultaneous equation solved every step, and it's where simulators differ most.
4. Integration — step velocities and positions forward by the timestep dt (semi-implicit Euler or Runge-Kutta variants).

Two practical consequences beginners hit immediately: the timestep is a stability knob (too large → energy appears from nowhere → the classic exploding robot), and real-time factor is not guaranteed — a complex scene may simulate slower than reality, and stepping faster than the physics can converge trades accuracy for speed.

What the robot is, to the engine

The simulator doesn't see your robot. It sees a list of rigid bodies — each with mass, a center of mass, and an inertia tensor — connected by joint constraints (hinge, slider, weld…), wearing two sets of geometry: collision shapes (simple, used by the physics) and visual meshes (pretty, physically ignored). That model arrives via a description file — URDF, SDF, or MJCF, compared here — and this is exactly why a wrong inertia value or a millimetres-instead-of-metres unit slip doesn't produce an error message: the engine faithfully simulates the absurd robot you described.

Contacts and friction — the hard 20% that causes 80% of weirdness

Rigid-body contact is mathematically nasty: infinitely stiff surfaces meeting means forces that would be instantaneous spikes. Every engine approximates — some allow tiny interpenetration with spring-like corrective forces (Gazebo/ODE style), MuJoCo uses a smooth, slightly-soft contact model that's fast and stable but lets grasped objects "swim" slightly, and each engine approximates the friction cone differently. Practical takeaways:

• Grasping and walking — the two most contact-rich behaviors — are precisely where simulators disagree with each other and with reality the most.
• Friction coefficients in description files are starting guesses, not truths; nobody's rubber wheel actually has mu = 1.0.
• Contact jitter (a resting object vibrating) usually means: timestep too large, mass ratios too extreme (a 1 g finger on a 100 kg arm), or bad inertia — validate first.

Actuation: how simulated motors work

Real joints are driven by motors with gearboxes, current limits, and delay. Simulators offer idealized drives — position servos, velocity drives, or direct torque — optionally shaped by PID gains, damping, and effort limits from your description file. The seductive trap: an ideal position servo achieves any target instantly with unbounded torque, so a controller tuned against it falls over on hardware. The closer you configure drives to your real actuators (effort limits, damping, control frequency), the more your tuning transfers. In the ROS world, ros2_control formalizes this boundary — the same controller code talks to simulated or real hardware through one interface.

Sensors: rendered, not sensed

Simulated cameras are just renders of the scene; lidars are ray casts; IMUs read the body's true acceleration plus configurable noise. Two things follow: simulated perception is optimistically clean unless you deliberately add noise, distortion, latency, and dropout — and camera/lidar simulation costs far more compute than physics, which is why perception-heavy training moved to GPU-parallel simulators (Isaac) that render thousands of environments at once.

Why sim works, then reality doesn't: the gap

The reality gap is the accumulated difference between the engine's idealizations and physics: unmodeled friction and backlash, motor dynamics, communication latency, sensor artifacts, and contact behavior. The working mitigations, in order of cheapness: honest parameters (measured masses, geometry-derived inertia, realistic drive limits), system identification (fit sim parameters until sim matches logged real trajectories), domain randomization (train policies across randomized physics so the real world looks like just another sample), and conservative control (margin for model error).

Choosing a simulator in 2026

They read different formats with different assumptions — the conversion traps are catalogued in the format comparison, and the converters on this site (URDF→MJCF, SDF→URDF, STEP→URDF) exist because of them.

Your first simulation, the right way

1. Model two links and one joint — not your whole robot. URDF basics here.
2. Give every link real mass and computed inertia (how). Validate.
3. Drop it into the simulator and just let it fall. If a passive pendulum swings plausibly and settles, your model and timestep are sane.
4. Add one actuator with realistic effort limits; tune position control on the single joint.
5. Only now scale up links, sensors, and controllers — one addition at a time, so when it breaks, you know what broke it.

Ready to go deeper than concepts? The robotics engineer roadmap lays out the full zero-to-mastery path — math, ROS 2, and the specialization tracks employers actually hire for.