What Is Sim-to-Real?

Sim-to-Real is the approach of training robot control policies in a physics simulator (such as MuJoCo or Isaac Gym/Lab) and then deploying them on real hardware. Training on real robots is slow, expensive, and breaks hardware; simulation runs thousands of robots in parallel at faster-than-real-time speed, which is what makes reinforcement learning of humanoid walking practical at all.

The Core Challenge: The Reality Gap

Simulation never matches reality exactly — friction, latency, motor characteristics, and sensor noise all differ. This mismatch is the sim-to-real gap. The main mitigations:

• Domain randomization: randomly perturb simulation parameters (mass, friction, latency) during training, forcing the policy to become robust;
• System identification: measure the real robot's motor response and inertial parameters accurately and write them back into the simulator;
• Actuator modeling: model the torque-speed behavior of the joint motors explicitly — often the deciding factor for legged-robot transfer.

Hardware Requirements

Deployed policies command joints at hundreds of Hz, demanding high-bandwidth force control (see quasi-direct drive and the MIT protocol) and low-latency buses. The BXI Elf 3 humanoid ships with a MuJoCo environment and ROS2 SDK, and its >1000 Hz PCIE-CANFD control architecture supports the full simulation-to-hardware workflow.