simtorealgap

Simtorealgap, commonly referred to as the sim-to-real gap or sim2real, denotes the discrepancy between system performance achieved in a simulated environment and performance in the real world. It arises because simulations cannot perfectly replicate real physics, sensor characteristics, actuation limits, and environmental variability. As a result, controllers, policies, or robotics systems that excel in simulation may underperform when deployed outside the virtual model.

The gap is a central concern in robotics, autonomous systems, and reinforcement learning, where development often occurs entirely in simulation before real-world deployment. Causes include mismatches in dynamics and friction, unmodeled contacts, latency and noise in sensing, imperfect rendering of visuals, and unforeseen disturbances. Even small inaccuracies in the simulator can compound over time, leading to large performance shortfalls after transfer.

Mitigation strategies aim to close the gap by increasing robustness and transferability. Domain randomization exposes models to a wide range of simulated variations during training, encouraging generalization to real-world diversity. System identification tunes simulator parameters to match observed real-world behavior. Higher-fidelity physics and sensor models, along with hybrid approaches that combine simulation with real data, also help. Further methods include continual or meta-learning, adaptation during deployment, and staged transfer with real-world fine-tuning.

Applications include robotic manipulation, legged locomotion, and autonomous driving, where simulation environments (such as Gazebo, MuJoCo, or PyBullet) are used to develop and test before real-world trials. Related concepts include domain adaptation and transfer learning, which address cross-domain generalization beyond robotics.