We identify every action, hand movement, object, and outcome, check the work, and deliver organized files your AI team can use.
We mark hands, objects, actions, outcomes, failures, retries, and recovery. Every label points back to the exact moment in the original video.
We package the approved work in the file structure and naming system your team already uses—without labeling the footage again.
RoboRun produces precise physical-AI annotations and makes them portable across teams, ontologies, model stacks, and training formats.
Turn video, state, actions, sensors, and telemetry into temporally precise annotations for robot learning. Every label can carry confidence, QA state, provenance, and its exact source window.
Translate existing annotations across taxonomies, label schemas, notation, episode boundaries, and model formats. The meaning, source offsets, and review history survive the conversion.
Video, state, actions, sensors, and telemetry align once. Annotation and translation reuse that source work instead of rebuilding the dataset for every output.
Start with the data and taxonomy you already have. RoboRun preserves the source, annotation meaning, and QA evidence while producing each downstream view.
Align video, state, actions, sensors, and telemetry so every annotation resolves to the exact moment and evidence that produced it.
Generate actions, objects, states, outcomes, failures, and CV outputs. Quality gates and review decisions stay attached to every record.
Map the same annotations into LeRobot, RLDS, Parquet, JSONL, custom schemas, policy episodes, RL environments, and evaluation slices.
Ladder preserves source identity, reusable encodings, decoded frames, features, and annotation provenance so every translation starts from the same verified record.
Every label, track, quality decision, and policy transition remains addressable against the same immutable source timeline.
Keep concepts, relationships, temporal boundaries, and review state explicit so annotations can move between schemas without becoming ambiguous.
Index, decode, and derive features once. Reuse them across annotation and translation jobs instead of repeatedly paying the media tax.
Ladder keeps source media reusable, applies deterministic quality gates first, and reserves expensive semantic judgment for the clips that actually need it.
One inspectable validation run with every hard failure and deferred decision tied back to source evidence.
Deterministic gates resolved the majority of the workload before the expensive model pass. The remaining 25.4% stayed available for semantic review.
Quality, CV, labels, training, and evaluation can reuse the same retained source work instead of repeatedly decoding and re-encoding it.
One annotation core.
Every model language.
Inspect the demo pipeline, or email us about annotations, schema translation, and a representative batch.
