Overview Video

This video gives a good overview of CoppeliaSim's capabilities. It however dates back a couple of years and many features have been added, improved or reworked.

Smart content & digital twins

CoppeliaSim can be used for quick application set-up, e.g. for simulation and/or configuration and monitoring of real hardware, and in general for digital twin applications.

Robot programming using Virtual Reality

The video illustrates the CoppeliaSim VR Interface that can visualize any CoppeliaSim scene in steamVR (openVR) compatible VR devices and return user manipulations to CoppeliaSim. Here the installer files (no compilation required, windows only). Here the instructions on how to use the interface.

Debugging Robot Path Generation

The video illustrates Coppelia Robotic's CoppeliaSim during path generation debugging for a PKM robot.

Navigation within a Point Cloud Environment

The video illustrates a CoppeliaSim simulation showing a mobile robot equipped with ultrasonic proximity sensors, and navigating via a simple Braitenberg algorithm inside of a point cloud environment, avoiding detected points. Next to meshes, octrees and point clouds can be used for collision detection, minimum distance calculation, and proximity sensor detection.

Reconfigurable Pick and Place Simulation

The video shows a pick and place simulation that can rapidly be reconfigured, in order to test various set-ups. The simulation runs on Process Simulate that connects to a headless instance of CoppeliaSim running major parts of the simulation. This demonstrator was created during a workshop jointly held between Siemens and Coppelia Robotics.

Velodyne Lidar

The video shows a simulation of the Velodyne HDL 64E S2 Lidar. CoppeliaSim supports efficient collision detection, minimum distance calculation and proximity sensor detection between meshes, octrees and point clouds.

Food Robotics: Parametrization of Robots

The video shows a CoppeliaSim simulation demonstrating the capability to deduce dynamics and loads applied to optimize the robots kinematics and drive system.

Adept Quattro 650HS & Food HandlingAdept Quattro 650HS & Food Handling

The video shows a CoppeliaSim simulation demonstrating the capability to deduce dynamics and loads applied to optimize the robots kinematics and drive system.


The video shows the Baxter robot doing some object manipulation. The simulation uses CoppeliaSim's built-in motion planning and inverse kinematics algorithms. The Baxter CAD data is courtesy of Rethink Robotics.

Cup Grasping and Handling (bis)

The video shows the Jaco and Mico arms grasping cups and pouring a liquid. The simulation uses CoppeliaSim's built-in motion planning and inverse kinematics algorithms. The simulation also features Vortex' superior grasping capabilities. The Jaco and Mico models are courtesy of Eric Rohmer, the Jaco and Mico CAD data is courtesy of Kinova Robotics.

Robots Playing Chess and Tic Tac Toe

This video shows an ABB IRB360 robot playing chess and Tic Tac Toe with three other robots. All 4 robots use image processing (blob detection) to track their opponent's moves. Robots play Tic Tac Toe on the first board by drawing circles or crosses with marker pens. On the second Tic Tac Toe board, robots play by milling material away. The original IRB360 and IRB140 CAD models are courtesy of ABB Robotics. There is however no link of any kind between ABB and CoppeliaSim or this simulation. The original Kuka YouBot CAD model is courtesy of Kuka. The hexapod robot model is courtesy of Lyall Randel. The chess figures are courtesy of J-m@n

Blob Detection and Pick and Place Demo

This video shows two ABB IRB360 robots that perform a pick-and-place task of colored shapes. The shape's position, orientation and color is extracted by a camera sensor that does blob detection. That information is then forwarded to the robots that correctly pick, orient, then place the shapes into containers. The blob detection is performed by a built-in filter component: in CoppeliaSim, image processing can be easily performed by simply combining several filters into an image processing block.

NAO's first steps in CoppeliaSim

The video shows the NAO robot doing its first steps in CoppeliaSim. The NAO model is courtesy of Marco Cognetti. The NAO meshes and movement data is courtesy of Aldebaran.

Ant-like Hexapod Robot

The video shows an ant-like robot walking. The whole control of the robot (as seen in this video) requires less than 200 lines of code, everything included. First the robot stays in place and changes the posture of its body. Then the robot walks straight, then sideways and backwards while keeping a constant body posture. In the third part, the robot walks straight while performing the same body movements as at the beginning. Finally, the robot rotates on the spot. Also notice how the head keeps its orientation independently of the body's posture. The ant model is courtesy of Lyall Randell.

ABB Fanta Can Challenge

This video demonstrates the simulation of 3 robots working together. It was realised based on this YouTube video. This simulation in CoppeliaSim runs without a single line of code. It should be noted that the variations displayed in the pin-cans minimum distance graphs are mainly due to the polygonal nature of the can models. The robot CAD models in this scene are courtesy of ABB Robotics. ABB Robotics has however no link of any kind with CoppeliaSim or this simulation.

Pick-and-Place Katana Robot

This video demonstrates the simulation of the Katana robot equipped with a paint nozzle. The paint nozzle disperses color with a gradient (center of the jet slightly brighter). All parameters can be fully customized (color, gradient, dispersion area, density, etc.). The simulation was set-up by simply copy, pasting and attaching the paint nozzle to the robot (no code modification is required, and the simulation is directly fully functional). The cables are handled through inverse kinematics (endpoints constraint, "desire of straightness" constraint, and obstacle avoidance constraint). Cable handling doesn't require a single line of code. The original CAD model is courtesy of Neuronics AG.


In this simulation a YouBot robot solves the problem of the tower of Hanoi (using cubes instead of discs however). The YouBot arm is controlled in forward and inverse kinematics (sometimes also linked to the mobile base). The position of the boxes is known at all time, but one could add a camera sensor to the simulation in order to also realistically extract their position from image processing. The original YouBot CAD model is courtesy of KUKA.

ACM-R5H: Snake-Like Amphibious Robot

The video shows CoppeliaSim simulating the ACM-R5H snake-like robot. Since CoppeliaSim version 2.4.5, swimming or flying robots can also be simulated. The ACM-R5H is controlled in a distributed fashion (each module has its own control script, like the real robot). The ACM-R5H CAD model is courtesy of Hibot Corporation.

Motion generation

This video demonstrates the Reflexxes Motion Library type IV, that is integrated in CoppeliaSim and fully functional. The On-line Trajectory Generation algorithms of the Reflexxes Motion Libraries are the first ones that allow computing jerk-limited robot motions from arbitrary initial states of motion while considering the current dynamic capabilities of robots. This is an important feature for classic industrial robot control architectures, servo drive control units, as well as for cutting edge robots with variable-stiffness or serial-elastic actuators in order to achieve deterministic reaction behaviors to sensor signals and events.

Object Tracking

The video shows several functionality of CoppeliaSim, but the focus is on the vision sensor functionality: in CoppeliaSim a vision sensor is similar to a camera, but many parameters can be adjusted (resolution, what objects are seen, etc.) and filters can be applied: CoppeliaSim offers more than 30 built-in filter components that can be combined in any way and that allow processing a vision sensor's image, and extracting useful information (e.g. center of gravity of a color patch, orientation, etc.).

Expliner: Power Line Inspection Robot

The video shows CoppeliaSim simulating the Expliner robot. Expliner (which is commercially available from Hibot Corporation) is able to inspect very high power lines while power is on, and also manages to overcome spacers and other obstacles on its way. The Expliner CAD model is courtesy of Hibot Corporation.

Holonomic Path Planning

This video shows two example simulations that use CoppeliaSim's path planning calculation module. It allows to define and solve holonomic tasks in 2-6 dimensions, and non-holonomic tasks for car-like vehicles. Path planning tasks can be executed directly, or accessed and executed through the API. The CAD data in the scene is courtesy of Lyall Randell, Cubictek Corp. and NT Research.

Interaction with Hardware

This video shows how a simulated industrial robot is controlled via a Wiimote device. CoppeliaSim can communicate (thus also control or be controlled) with virtually any type of hardware through socket communication, pipe communication, serial port communication, etc. If a means of communication is currently not supported, then anyone can write a plugin that will support that means of communication.

Simulation of a Worm-like Robot

This video shows a worm-like robot simulated in CoppeliaSim. The robot is composed by 49 segments and 48 active joints. Each segment has a pair of passive wheels under its belly. The worm is able to avoid obstacles (thus also self-collision) using an invisible proximity sensor mounted on the first segment. The whole control of the robot requires less than 100 lines of code.

Simulation of a Pipe Inspection Robot

The video shows the simulation of PipeTron, a pipe inspection robot. It also illustrates the new camera function (since CoppeliaSim 2.5.12) that automatically fits robots or objects in the camera view. The CAD data of the PipeTron model is courtesy of Hibot Corporation.

Cup Grasping and Handling

The video shows a manipulator grasping a cup and pouring the liquid into another cup. The simulation is courtesy of Ferdian Adi Pratama.

Car Simulation

The video demonstrates 2 realistically simulated vehicles (i.e. front/rear spring/damper, Ackermann steering, etc.), and a humanoid robot. The humanoid robot unfortunately gets hit by one of the cars... tragic!