AiVibeSim-to-Real Transfer
Sim-to-real transfer is the process of moving a robot policy or perception model trained in simulation onto physical hardware while preserving its performance, despite the differences between simulated and real worlds known as the reality gap.
Simulators differ from reality in physics, such as friction, contact and actuator dynamics, and in sensing, such as lighting, textures and noise. Domain randomisation varies these parameters widely during training so that the real world appears as one more variation. System identification measures real parameters to make the simulator more faithful, and domain adaptation aligns simulated and real data. Fine-tuning on a small amount of real data is also common.
Sim-to-real makes reinforcement learning practical for robots, enabling locomotion, dexterous manipulation and grasping policies trained over very large numbers of simulated episodes. Synthetic images rendered in simulation are also used to train vision models for detecting parts and defects when real images are scarce.
Transfer success must be measured on the real system across representative conditions. Contact-rich tasks and deformable objects remain difficult to simulate. GPU-accelerated simulators run many environments in parallel, shortening training time. Deployment on physical machines still requires conventional safety measures, staged testing at reduced speed and oversight by qualified personnel.
Key points
- Moves policies and models trained in simulation onto real robots
- The reality gap covers physics, sensing and actuation differences
- Domain randomisation and system identification narrow the gap
- Performance must be verified 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.
Related terms
- Robot SimulationRobotics & Physical AI
- Reinforcement LearningRobotics & Physical AI
- Physical AIRobotics & Physical AI
- Imitation LearningRobotics & Physical AI
- Digital TwinIndustrial IoT & Edge