<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Domain Adaptation on WorldSense Tech Blog</title><link>https://worldsensetech.com/en/tags/domain-adaptation/</link><description>Recent content in Domain Adaptation on WorldSense Tech Blog</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Wed, 05 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://worldsensetech.com/en/tags/domain-adaptation/index.xml" rel="self" type="application/rss+xml"/><item><title>Is Sim-to-Real Too Hard? World Model-Driven Adaptive Transfer Methods</title><link>https://worldsensetech.com/en/articles/sim-to-real-transfer/</link><pubDate>Wed, 05 Aug 2026 00:00:00 +0000</pubDate><guid>https://worldsensetech.com/en/articles/sim-to-real-transfer/</guid><description>&lt;p&gt;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&amp;rsquo;re diving into a more practical topic: how do we actually deploy policies trained in simulation onto real robots?&lt;/p&gt;
&lt;p&gt;This problem has plagued the robotics AI field for nearly a decade. World models perform brilliantly in simulated environments — DreamerV3&amp;rsquo;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 &amp;ldquo;Sim-to-Real Gap&amp;rdquo;.&lt;/p&gt;</description></item></channel></rss>