World Model on WorldSense Tech Blog

What Does a World Model Actually Do in a Robot? From Perception to Action

Wed, 26 Aug 2026 00:00:00 +0000

Dreamer Series · Part 2

Part 1 broke down Dreamer’s overall architecture. This article zooms out one step further: where exactly does a world model sit in a robotic system? What happens between sensor data and the final action? Rather than discussing how to train a general-purpose robot world model, this article focuses on its functional position within robotic systems.

1. A Fundamental Difference: Robot World Models Are Nothing Like Language Models

Over the past few years, the word “model” has been used repeatedly across AI. Language models predict the next token, vision models predict the next frame, autonomous driving models predict the behavior of traffic participants. They all do some form of “prediction,” but the objects and constraints differ enormously.

Understanding RSSM Through Code (6): Default Config, Four Formulas, and the Code↔Math↔Semantics Map

Mon, 24 Aug 2026 00:00:00 +0000

Understanding RSSM Through Code · 第 6 篇 / 共 6 篇

Series contents (you are on part 6, bolded; prev/next at the bottom):

(1) Where RSSM Sits & the Stochastic State

(2) Prior/Posterior, Straight-Through & unimix

(3) _core(), deter=8192 & Block GRU

(4) KL Balancing, Free Nats & Final KL

(5) Imagine, Observe vs. Imagine & Reset 6. (6) Default Config, Four Formulas & the Map

25. Default Config: Split into “RSSM Architecture” and “World-Model Training”

To avoid mixing “RSSM architecture parameters” with “world-model / agent training config,” the configuration is split into two tables below.

Understanding RSSM Through Code (5): Imagine, Observe vs. Imagine, Sequence Training, and Reset

Sun, 23 Aug 2026 00:00:00 +0000

Understanding RSSM Through Code · 第 5 篇 / 共 6 篇

Series contents (you are on part 5, bolded; prev/next at the bottom):

(1) Where RSSM Sits & the Stochastic State

(2) Prior/Posterior, Straight-Through & unimix

(3) _core(), deter=8192 & Block GRU

(4) KL Balancing, Free Nats & Final KL 5. (5) Imagine, Observe vs. Imagine & Reset

(6) Default Config, Four Formulas & the Map

20. Imagine: How Does RSSM Run Without Observations?

This is the most elegant part of RSSM.

Understanding RSSM Through Code (4): KL Balancing, Free Nats, and the Final KL Combination

Sat, 22 Aug 2026 00:00:00 +0000

Understanding RSSM Through Code · 第 4 篇 / 共 6 篇

Series contents (you are on part 4, bolded; prev/next at the bottom):

(1) Where RSSM Sits & the Stochastic State

(2) Prior/Posterior, Straight-Through & unimix

(3) _core(), deter=8192 & Block GRU 4. (4) KL Balancing, Free Nats & Final KL

(5) Imagine, Observe vs. Imagine & Reset

(6) Default Config, Four Formulas & the Map

14. Complete Observe Phase Data Flow (Observe / Imagine in One Framework)

We can now draw the RSSM state-transition framework and put Observe and Imagine inside the same _core() transition framework:

Understanding RSSM Through Code (3): Deterministic Transition _core(), deter=8192, and Block GRU

Fri, 21 Aug 2026 00:00:00 +0000

Understanding RSSM Through Code · 第 3 篇 / 共 6 篇

Series contents (you are on part 3, bolded; prev/next at the bottom):

(1) Where RSSM Sits & the Stochastic State

(2) Prior/Posterior, Straight-Through & unimix 3. (3) _core(), deter=8192 & Block GRU

(4) KL Balancing, Free Nats & Final KL

(5) Imagine, Observe vs. Imagine & Reset

(6) Default Config, Four Formulas & the Map

9. The Real Deterministic Transition: `_core()`

This is the most worthwhile part of rssm.py to read.

Understanding RSSM Through Code (2): Prior/Posterior, Straight-Through Sampling, and unimix

Thu, 20 Aug 2026 00:00:00 +0000

Understanding RSSM Through Code · 第 2 篇 / 共 6 篇

Series contents (you are on part 2, bolded; prev/next at the bottom):

(1) Where RSSM Sits & the Stochastic State 2. (2) Prior/Posterior, Straight-Through & unimix

(3) _core(), deter=8192 & Block GRU

(4) KL Balancing, Free Nats & Final KL

(5) Imagine, Observe vs. Imagine & Reset

(6) Default Config, Four Formulas & the Map

4. Translating the Source Code into Mathematical Formulas (Watch the Time Index)

DreamerV3’s recurrence relation:

Understanding RSSM Through Code (1): Where RSSM Sits in DreamerV3 and the Stochastic State

Wed, 19 Aug 2026 00:00:00 +0000

Understanding RSSM Through Code · 第 1 篇 / 共 6 篇

Series contents (you are on part 1, bolded; prev/next at the bottom): 1. (1) Where RSSM Sits & the Stochastic State 2. (2) Prior/Posterior, Straight-Through & unimix 3. (3) _core(), deter=8192 & Block GRU 4. (4) KL Balancing, Free Nats & Final KL 5. (5) Imagine, Observe vs. Imagine & Reset 6. (6) Default Config, Four Formulas & the Map

Source Code Reading Guide

VLA vs World Models: Which Will Prevail?

Tue, 04 Aug 2026 00:00:00 +0000

Between 2025 and 2026, two distinctly different technical directions have emerged in robot AI. One is the VLA (Vision-Language-Action) approach, represented by RT-2, OpenVLA, and pi-0. The other is the World Models approach, represented by DreamerV3, Genie, and DIAMOND.

Many colleagues have asked me: between these two directions, which one should I bet on? My answer is: the question itself is wrong.

In today’s article, I want to break down and compare these two approaches, explain the logic, advantages, and bottlenecks of each, and then discuss why I believe they will ultimately converge.

What Is a Robot World Model? An Engineer's Deep Dive

Sat, 01 Aug 2026 00:00:00 +0000

If you’ve been following the latest developments in AI, you may have noticed a trend: from ChatGPT to Sora, from AlphaFold to robotic manipulation, AI is moving from “understanding language” to “understanding the world.” At the heart of this transition lies an increasingly central concept — the World Model.

In today’s post, I want to discuss, from an engineer’s perspective, what a world model is, why it matters so much for robotics, and what DreamerV3 — currently one of the most representative approaches — actually does.