For an AI system to control a robot that can pick up an object, use a tool, or perform tasks in ways similar to humans, algorithms alone are not enough.
AI needs data to learn.
Saltlux Technology provides data collection and dataset development services for Physical AI & Robotics, covering everything from carefully designed and controlled tasks in laboratory environments to data collection directly in real-world settings.
Starting with Human Actions
A task that seems simple to a human can contain a significant amount of information for AI.
When we see an object, reach toward it, pick it up, and move it somewhere else, our body continuously coordinates perception, judgment, and movement.
For Physical AI, these processes can be transformed into data that helps machines learn how to perceive and act in the physical world.
In the Lab, our team can design controlled data collection scenarios by defining object positions, object types, tasks to be performed, the number of repetitions, and variations in operating conditions.
This controlled environment enables data to be collected systematically and makes quality inspection and validation more efficient.
But the Real World Is Not a Lab
A model that performs well in a controlled environment may not necessarily perform equally well when deployed in the real world.
In real-world environments, lighting conditions change, objects can appear in unexpected positions, spaces may be constrained, and many surrounding factors can be difficult to predict.
That is why, in addition to Lab-based collection, Saltlux Technology conducts data collection across a variety of real-world environments.
From retail environments with hundreds of different products…
…to factories and workshops filled with machinery, tools, and materials…
…as well as vehicles and outdoor environments.
Each environment introduces new conditions and scenarios that AI systems may encounter when deployed in real-world applications.
A Data Pipeline from the Lab to the Real World
Our service can be summarized as the following workflow:
Task Design
→ Lab Data Collection
→ Validation & Refinement
→ Real-world Data Collection
→ Data Processing & Quality Control
→ Dataset
A dataset can simply be understood as a structured and standardized collection of data prepared for AI research, development, or training.
By combining Lab Data Collection and Real-world Data Collection, Saltlux Technology aims to build datasets that are not only large in scale but also diverse enough to support a wide range of applications in Physical AI, Computer Vision, and Robotics.
From the Lab to the real world, data is the bridge that brings AI closer to the ability to perceive, understand, and act in the physical world.
