Yeongseung's Blog

Zero to SLAM

Fri, 03 Apr 2026 19:15:23 +0900

Introduction

I’ve always been eager to dive into the fundamentals of robotics, and I finally have the opportunity through my “Autonomous Mobile Robot” course. I decided to write this blog post not only to solidify what I’ve learned but also to create a practical reference for my future work.

This post focuses on the essential keywords of autonomous systems: Perception, Navigation, Localization, Planning, and SLAM.

At its core, an “autonomous robot” is defined by its ability to:

Statistical Notes: Questions, Clarifications, and Intuition

Thu, 05 Mar 2026 17:10:26 +0900

Introduction

This blog reflects my journey through Machine Learning, Reinforcement Learning, and Robotics. While my current work spans these areas, my academic roots are in Statistics, a foundation that continues to shape how I think about problems. From a theoretical standpoint, I believe Statistics remains the core foundation of modern machine learning.

Over time, while studying statistics, I often encountered concepts that felt confusing, subtle, or easy to misunderstand. Some of them became clearer after revisiting the ideas multiple times or thinking about them from different perspectives, while others are still things I occasionally find myself reflecting on.

Understanding and Building the Transformer

Tue, 24 Feb 2026 19:51:23 +0900

Introduction

The transformer architecture has become the foundation of modern deep learning, powering everything from language models to vision models. Since I’ll be referencing transformers frequently in future posts, I wanted to create my own comprehensive analysis of the architecture. In this post, I’ll walk through building a transformer-based translation model step by step, following the original paper by Vaswani et al. (2017)¹. We’ll dive into the code, understanding each component as we implement it.

Stabilizing Q-Learning in CartPole

Tue, 20 Jan 2026 16:14:29 +0900

Introduction

If you are interested in reinforcement learning, you have probably heard of CartPole [6], often considered the “Hello World” environment of the field. This is partly because the task is simple to understand, and partly because each episode ends quickly, allowing you to observe learning progress within a short time.

Figure 1. CartPole environment visualization

Looking back, I have run many experiments in CartPole before, but not in a structured or systematic way, which caused me to forget most of the details over time. In this post, I aim to consolidate those trials into a coherent narrative, focusing in particular on why Q-learning can become unstable and how techniques such as experience replay, Double DQN, and failure-focused sampling can improve robustness.

Generating Synthetic Datasets using Isaac Sim

Mon, 05 Jan 2026 10:58:31 +0900

Codes

Intro

For vision tasks in ML, we naturally need images and corresponding labels to train a model. Normally, we consider two options. searching and finding datasets we can leverage or creating datasets by taking a photos, labeling manually. The latter is rarely feasible, especially for a student or a small team, given the immense amount of time required for labeling.

Figure 1. Isaac Sim GUI

Here’s a third option, which is to utilize Isaac Sim(simulation program from NVIDIA) to generate synthetic datasets. Initially, I was kind of skeptical about whether a vision model trained on simulated images would actually perform well in real environments. However, This post changed my mind. I recommend you to read it.