GitHub - Hebbian-Robotics/hflow: SDK for robotics teams to verify the quality of their data used for AI model training.

It makes data tooling and practices typically developed inside large robotics teams accessible to teams of any size. We believe processing data is a major bottleneck in robotics. A corpus can combine video, state, actions, timestamps, and metadata from many recording systems. Teams often feel the problem first in quality control: determining whether cameras froze, streams drifted out of sync, required topics disappeared, or duplicate recordings entered the corpus. As the corpus grows, fragmented scripts make it difficult to know what ran, audit the results, or reproduce a dataset. Teams can start with HFlow's built-in checks, write new transformations, checks, labels, and enrichments, or connect processing code they already use. HFlow handles the orchestration, storage, versioning, and curation around those steps. HFlow stamps each processed episode with its provenance, renders the pipeline as a graph, and records metadata and quality evidence in a queryable catalog. You can trace how outputs were produced, monitor every stage, and investigate a corpus without loading the underlying recordings. MCAP is HFlow's v1 input and output boundary because it efficiently stores and serves synchronized video, state, action, and other time-series streams. That format requirement does not define where the data comes from: human-worn cameras, teleoperated robots, autonomous policies, and other collection systems can all feed the pipeline once their data is represented as a supported MCAP episode. Status: pre-v1, with the core lifecycle working end to end. HFlow is ready to try locally. See what is implemented and open issues for current details and remaining work.