CoppeliaSim is a general-purpose robot simulator that models robots, sensors, actuators and their environment in one scene and lets each object be controlled by its own embedded script or by an external program. In robotics simulation it is known for a distributed control architecture, a choice of interchangeable physics engines and a broad set of built-in tools for kinematics, path planning and sensing.
The software was previously released under the name V-REP (Virtual Robot Experimentation Platform), and older tutorials, models and research papers still use that name. It is used in education, research prototyping and industrial cell design, where engineers need to test robot control logic before touching physical hardware.
How CoppeliaSim structures a simulation
A CoppeliaSim simulation is built from a scene hierarchy of objects: shapes, joints, dummy frames, sensors, cameras, lights and paths, arranged in a parent-child tree that defines how poses propagate. A complete scene is stored as a .ttt file, while a reusable robot or tool is stored as a model file with the .ttm extension, so a gripper or mobile base can be dropped into many scenes.
Control is distributed across objects. Each robot can carry its own simulation script that runs in step with the simulation loop, and customization scripts can run even when the simulation is stopped, for example to build a configuration dialog. This design lets a scene with ten robots keep ten independent controllers instead of one monolithic program.
Each simulation step advances simulated time by a fixed increment, commonly 50 ms by default, while the selected physics engine may subdivide that step into several smaller internal steps for stable contact handling. Shorter steps improve accuracy for fast mechanisms but increase computation time roughly in proportion.
The table below summarizes the main object types and the role each plays in a scene.
| Object type | Role in the scene | Typical use |
|---|---|---|
| Shape | Rigid body with visual and collision geometry | Robot links, obstacles, parts on a conveyor |
| Joint | Revolute, prismatic, spherical or screw constraint | Motor axes driven in position, velocity or torque mode |
| Dummy | Massless reference frame | Tool center points, inverse kinematics targets |
| Proximity sensor | Geometric volume that detects entities | Ultrasonic or infrared range sensing |
| Vision sensor | Rendered camera image or depth buffer | RGB and depth cameras, simple line detection |
| Force sensor | Measures forces and torques between two objects | Wrist sensors, breakable connections |
Physics engines and control interfaces
CoppeliaSim does not depend on a single solver. The user selects a physics engine per scene, which makes the simulator useful for checking whether a result depends on solver behavior rather than on the robot design. Supported engines include Bullet, ODE, Newton, Vortex and MuJoCo, each with different strengths in contact stability, speed and joint accuracy.
| Engine | Strength | Typical caution |
|---|---|---|
| Bullet | Fast, widely used rigid-body dynamics | Grasping can slip without tuned friction |
| ODE | Mature and predictable for simple mechanisms | Stiff contacts may jitter at large steps |
| Newton | Stable joint chains and stacking | Fewer tuning options in practice |
| Vortex | High-fidelity industrial contact and friction | Requires a separate commercial license |
| MuJoCo | Accurate articulated dynamics and soft contacts | Model parameters differ from other engines |
External programs connect through several interfaces. The ZeroMQ remote API exposes nearly the full scripting interface to clients written in Python, C++, Java, MATLAB, Octave and other languages, and supports a stepping mode in which the client triggers each simulation step for deterministic co-simulation. A ROS 2 interface plugin publishes topics and services so the simulated robot can replace a real one in a ROS 2 graph.
Embedded scripts are written in Lua or Python, and C++ plugins extend the simulator with new commands or custom dynamics. Built-in modules provide inverse kinematics, collision and distance checking, and path planning through an OMPL-based plugin.
How CoppeliaSim compares with other robot simulators
CoppeliaSim sits between lightweight physics libraries and large rendering platforms. Its main distinctions are the all-in-one editor, the per-object scripting model and the multi-engine design, while competitors often focus on one ecosystem or on photorealism.
| Simulator | Main focus | Difference from CoppeliaSim |
|---|---|---|
| Gazebo | Open-source simulation tightly integrated with ROS | Configured mainly through SDF files rather than an interactive editor |
| Webots | Open-source simulator for education and research | Single built-in physics engine and controller-per-robot processes |
| Isaac Sim | GPU-accelerated, photorealistic simulation | Needs a capable NVIDIA GPU and targets synthetic data and learning |
| PyBullet | Python physics library for learning research | No full scene editor and fewer built-in sensors |
| MuJoCo | Fast, precise dynamics engine | A physics engine that CoppeliaSim can use internally |
The choice often depends on the downstream workflow. Teams committed to ROS tooling may prefer Gazebo, reinforcement learning at massive scale favors GPU-parallel tools such as Isaac Lab, and rapid prototyping of manipulation cells with mixed sensors is a frequent reason to choose CoppeliaSim.
Practical setup and configuration choices
Good results start with separate collision and visual geometry. Detailed meshes imported from CAD in STL, OBJ or COLLADA format are kept for rendering, while simplified convex or primitive shapes handle collisions, which keeps contact computation fast and stable. Robots described in URDF can be imported and then refined in the editor.
The following decision table connects common project goals with suitable configuration choices.
| Project goal | Recommended setting | Reason |
|---|---|---|
| Testing grasping | Primitive collision shapes, smaller time step | Reduces contact jitter and object slipping |
| Deterministic co-simulation | Remote API in stepping mode | The external controller and simulator advance together |
| Batch experiments | Headless mode without the GUI | Saves rendering time on servers |
| ROS 2 integration | ROS 2 interface plugin with simulation time | Keeps timestamps consistent across nodes |
| Solver sensitivity check | Run the scene with two engines | Reveals results that depend on the engine |
Limitations and common mistakes
Like every simulator, CoppeliaSim is subject to the reality gap: friction, motor saturation, backlash, sensor noise and cable forces are simplified or missing unless modeled explicitly. Techniques such as domain randomization and hardware-in-the-loop testing help close that gap but never remove it.
- Unrealistic mass and inertia values cause joints to oscillate or explode; derive them from CAD or compute them from the collision shape.
- Using complex non-convex meshes for collisions slows the simulation sharply and makes contacts unstable.
- Running a remote client without stepping mode lets the simulation drift ahead of the controller, which hides timing bugs.
- Assuming that vision sensor images are photorealistic can mislead perception testing, because the rendering is functional rather than physically based.
- Ignoring license terms: the free educational edition is not intended for commercial work.
The idea in brief
CoppeliaSim is a virtual workshop for robots. An engineer builds a digital copy of a robot on a computer. The copy is placed in a virtual room. It then moves according to the same control program that the real robot will later use.
The software calculates how parts fall, collide and grip. It also imitates sensors such as cameras and distance detectors. Mistakes cost nothing in the virtual world. Ideas can be tried, broken and fixed quickly before any metal is cut.
A practical scenario
A small integration team is designing a pick-and-place cell in which a six-axis arm moves parts from a conveyor into trays. Before ordering hardware, the engineers import the arm from URDF, add a conveyor, a vision sensor above the belt and a suction gripper, and drive the scene from a Python program through the ZeroMQ remote API in stepping mode.
Running the cell with the Bullet engine and then with MuJoCo shows that parts occasionally slip off the tray edge in both cases, which points to the placement trajectory rather than the solver. The team lowers the release height and adds a short dwell time. After a few hundred simulated cycles, the cycle time settles near the target, and the same control code transfers to the physical cell with only calibration changes.