<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tutorial on WorldSense Tech Blog</title><link>https://worldsensetech.com/en/categories/tutorial/</link><description>Recent content in Tutorial on WorldSense Tech Blog</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Sat, 15 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://worldsensetech.com/en/categories/tutorial/index.xml" rel="self" type="application/rss+xml"/><item><title>Isaac Lab Installation Guide: From Zero to Running on AutoDL</title><link>https://worldsensetech.com/en/articles/isaac-lab-install-guide/</link><pubDate>Sat, 15 Aug 2026 00:00:00 +0000</pubDate><guid>https://worldsensetech.com/en/articles/isaac-lab-install-guide/</guid><description>&lt;p&gt;The previous article covered what Isaac Lab is and what it can do. But if you&amp;rsquo;ve actually tried installing it, you know there&amp;rsquo;s quite a gap between &amp;ldquo;cloning the repo&amp;rdquo; and &amp;ldquo;running the examples.&amp;rdquo;&lt;/p&gt;
&lt;p&gt;This article documents my complete process of installing Isaac Lab on an AutoDL cloud GPU server (RTX 5090D / 32GB). Every error mentioned here was genuinely encountered, and every solution was verified in practice. Hopefully this saves you a few hours of debugging.&lt;/p&gt;</description></item><item><title>How to Get Started with Reinforcement Learning: A Practical Guide</title><link>https://worldsensetech.com/en/articles/reinforcement-learning-how-to-start/</link><pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate><guid>https://worldsensetech.com/en/articles/reinforcement-learning-how-to-start/</guid><description>&lt;p&gt;I have a deep appreciation for this question. I transitioned from traditional automation into reinforcement learning myself, and I stumbled through plenty of pitfalls along the way. Here&amp;rsquo;s the path I&amp;rsquo;ve found most effective.&lt;/p&gt;
&lt;h2 id="first-things-first-what-do-you-want-to-do-with-reinforcement-learning"&gt;First Things First: What Do You Want to Do with Reinforcement Learning?&lt;/h2&gt;
&lt;p&gt;Reinforcement learning spans a wide range of application domains, and the learning path differs for each:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Games and simulation&lt;/strong&gt;: Atari games, MuJoCo robot simulations — the most beginner-friendly with the most resources available.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Robotics control&lt;/strong&gt;: Robotic arms, quadruped robots, humanoid robots — requires combining simulation with Sim-to-Real transfer.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Recommender systems and advertising&lt;/strong&gt;: The primary application scenario for internet companies — more engineering-focused, with relatively lower math requirements.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Autonomous driving&lt;/strong&gt;: Decision-making and planning modules — requires integration with classical control theory.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Decide on your direction first, then choose your learning path accordingly — it makes a huge difference in efficiency. The advice below follows &amp;ldquo;robotics control&amp;rdquo; as the main thread, since that&amp;rsquo;s the area I know best and one of the most promising directions today.&lt;/p&gt;</description></item></channel></rss>