Overview

Mosaico Alchemy is a collection of ready-to-use data ingestion pipelines for Physical AI and Robotics. It is organized in Packs, where each Pack targets a specific domain (like Robotic Manipulation) and translates heterogeneous dataset formats into the ontology used by the Mosaico SDK.

Why we built it

If you have ever tried to combine datasets from different sources, you already know how this goes. One dataset is a ROS .bag from a lab experiment. Another is an HDF5 archive from a simulation run. A third comes from a hardware vendor with a custom binary format and a PDF that vaguely describes the schema. They all contain robot data, but getting them into a shape where you can actually compare or join them is a project in itself.

Most of that work is not interesting. It is timestamp reconciliation, coordinate frame alignment, figuring out whether that vel field is in m/s or mm/s, and writing throwaway scripts you will never look at again.

The deeper point is that this problem is not really about file formats. It is about the fact that the same physical concept, a robot's pose, a joint angle, a camera frame, gets expressed differently by every team and every tool. Alchemy's goal is to show that it is possible to bring all of that under a single, coherent representation. Once data from different sources speaks the same ontology, you can run the same queries against all of it, build datasets that span multiple collection pipelines, and stop worrying about the plumbing every time you add a new source.

Each Pack is also a concrete entry point into Mosaico's data pipeline. If you are working with a known dataset format, like those covered in the Robotic Manipulation pack, you can use Alchemy to load it directly into the platform without writing any ingestion code yourself. From that point on, the full Mosaico SDK is available: query across sequences, stream specific topics, filter by sensor values, and feed data straight into your training pipeline.