Robot Simulation

Robot simulation models a robot, its environment and often its physics in software, so engineers can check reach, collisions and cycle times, test programs, and train or validate AI policies before running anything on real hardware.

A simulator combines kinematic and dynamic models of the robot with models of grippers, parts and fixtures, and often a physics engine that computes contact, friction and gravity. Sensor models can render camera images, depth data and lidar scans. Industrial offline programming tools emphasise accurate controller behaviour and cycle times, while research and AI simulators emphasise physics, sensor realism and running many scenarios in parallel.

Uses include cell layout and feasibility studies, cycle-time estimation, collision checking, virtual commissioning against PLC logic, operator training, and generating training data or training policies with reinforcement learning. Examples include the open-source Gazebo and MuJoCo simulators and NVIDIA Isaac Sim.

A simulation is only as good as its models. Contact dynamics, cables, deformable objects and sensor noise are hard to model, which creates the reality gap addressed by sim-to-real techniques. Results should be validated on the physical system, and safety validation must always be carried out on the real installation.

Key points

Models robot kinematics, dynamics, sensors and the surrounding cell
Used for layout, cycle time, collision checks and virtual commissioning
Physics simulators train and test AI policies at scale
Results must be validated on real hardware

Where AiVibe comes in

AiVibe designs and manufactures the AiAmbA AI Factory, whose edge devices and AI agents let people talk to robot controllers in plain language. Robotics perception is an AiAmbA use case, and robot safety functions follow ISO 10218 and never depend on the AI layer.

Explore AiVibe’s work in Robotics & Physical AI →

Related terms

Terms that refer to Robot Simulation

Robot Operating System (ROS)Robotics & Physical AI