Domain Randomization: The Bridge from Simulation to Reality

Sat, 08 Aug 2026 00:00:00 +0000

In the previous post, we discussed how TD-MPC uses world models for robot control. But whether you’re using Dreamer, TD-MPC, or any other method, the learned policy ultimately needs to be deployed on a real robot. This inevitably leads to Sim-to-Real transfer — and Domain Randomization is the most fundamental technique on this path.

This article systematically breaks down domain randomization: what problem it solves, what types exist, how to implement it in engineering, and the latest advances.

Is Sim-to-Real Too Hard? World Model-Driven Adaptive Transfer Methods

Wed, 05 Aug 2026 00:00:00 +0000

This is the fourth article in the World Models series. The first three covered the basic concepts of world models, the core principles of RSSM, and an introduction to embodied AI. Today, we’re diving into a more practical topic: how do we actually deploy policies trained in simulation onto real robots?

This problem has plagued the robotics AI field for nearly a decade. World models perform brilliantly in simulated environments — DreamerV3’s sample efficiency is 10-100x higher than traditional RL. But once deployed on real robots, performance often drops by 30-50%. This gap is the famous “Sim-to-Real Gap”.

Sim-to-Real on WorldSense Tech Blog

Domain Randomization: The Bridge from Simulation to Reality

Is Sim-to-Real Too Hard? World Model-Driven Adaptive Transfer Methods