For an AI system to control a robot that can pick up an object, use a tool, or perform a task in a human-like way, 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 the lab to data collection in real-world environments.
Starting with Human Actions
A task that seems simple to a person can contain a great deal of information from an AI perspective.
When we see an object, reach for it, pick it up, and move it somewhere else, our bodies continuously coordinate 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 data collection scenarios under controlled conditions, defining factors such as object positions, object types, tasks to be performed, repetition counts, and variations in each scenario.
This makes it possible to collect data systematically while also making quality inspection and validation more manageable.
But the Real World Is Different from the Lab
A model that performs well in a controlled test environment may not perform equally well once it is deployed in the real world.
In real-world settings, lighting conditions change, objects can appear in unexpected positions, spaces may be constrained, and the surrounding environment often contains unpredictable factors.
That is why, in addition to lab-based collection, Saltlux Technology also conducts data collection across a variety of real-world environments.
From retail stores with hundreds of different products…
…to factories filled with machinery, tools, and materials…
…as well as vehicles and outdoor environments.
Each environment introduces additional situations that AI systems may encounter when deployed in real applications.
A Data Pipeline from the Lab to the Real World
Our service can be understood as a structured pipeline:
Task Design
→ Lab Data Collection
→ Review & Adjustment
→ Real-world Data Collection
→ Data Processing & Quality Control
→ Dataset
A dataset here refers to an organized and standardized collection of data prepared for AI research or model 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.
