VLA on WorldSense Tech Blog

The Data Challenge in Robotics: Where Does Robot Learning Data Come From?

Thu, 13 Aug 2026 00:00:00 +0000

The development of large language models has demonstrated that large-scale, diverse data can significantly improve model capabilities. But data scale is only one piece of the puzzle. The Transformer architecture, pre-training objectives, scaling laws, and post-training methods like RLHF all work together to produce today’s LLMs.

But if you’ve worked on robotics AI, you know this firsthand: robot data is far harder to come by than language data.

Why is that? What exactly makes robot data so difficult? And are there solutions?

World Models as Synthetic Data Engines for VLA Training

Thu, 06 Aug 2026 00:00:00 +0000

In the previous post, we set up and ran DreamerV3 from scratch. A reader asked: what can a trained world model actually do?

Today we discuss a more cutting-edge topic: how to use world models to generate synthetic data that enhances the training of VLA (Vision-Language-Action) models. This has become one of the widely researched directions in robot foundation models in recent years.

Why VLA Needs Synthetic Data

The core capability of a VLA is enabling a robot to “understand human language” — you point at a cup on the table and say “hand me the red one,” and it can understand the language instruction and execute the corresponding action.

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.