Every failure.
A learning signal.
Every robot failure is a training signal. Robot Ops turns raw robot logs into replayable incidents, AI-assisted triage, and a labeled ML dataset — the data flywheel that turns your robot's failures into your model's training advantage. Built on MCAP and Foxglove, deployed with a US robotics operator, with a runnable end-to-end demo.
An incident becomes
the next learning cycle.
Follow one illustrative failure window from raw logs to a reviewed dataset. Hypotheses stay separate from confirmed labels.
Find the moment that matters.
Detect a candidate event window and gather the synchronized context a reviewer needs.
Replay the evidence together.
Bring the observation, robot state, and proposed explanation into the same review. A generated hypothesis is a starting point for an engineer.
Keep the human-reviewed signal.
Accept or correct the event label, export a curated example, and track its lineage into the training workflow.
Raw logs in. A labeled dataset out.
Modern robotics teams have the logs — they don't have the pipeline to find the failures, replay them, explain what happened, and get structured labels into the hands of the ML team. Robot Ops is that pipeline.
It detects the incident window, replays the clean-vs-corrupted signal diff in Foxglove, generates a triage hypothesis with investigation steps, and exports a curated, annotated failure dataset — end to end.
Find the failure, not the haystack.
Detects the incident window across signals — a lidar fault, a navigation deviation, a perception dropout — so nobody scrubs hours of logs by hand looking for the moment it went wrong.
Replay the exact moment.
Records both clean and corrupted signals to MCAP so your team can replay the incident in Foxglove — clean vs. corrupted, side by side — where the robotics ecosystem already converges for debugging.
A hypothesis, not a black box.
Claude produces a triage hypothesis and investigation steps for each incident, tied to the evidence. Output is cached and human-reviewed — never a live call in the loop, and never a "root cause" verdict your engineers can't check.
Failures become training data.
Frames around each incident are ingested into FiftyOne and exported as CVAT annotation tasks — a labeled, queryable failure dataset ready for your ML team, not a folder of unsorted images.
Robot Ops ships with a runnable, end-to-end reference demo: a TurtleBot3 runs a warehouse mission in ROS 2 and Gazebo, a deterministic lidar fault is injected mid-run, the whole run records to MCAP, and the pipeline detects the incident, replays it in Foxglove, generates the triage hypothesis, and exports the annotated dataset — the full loop, on a dashboard you can open.
Robot Ops is deployed with a US robotics operator under NDA, and ships with a runnable reference demo of the full pipeline. It's US-focused today. We stand it up against your fleet's own data, document it, and hand it over — and can operate it for you if you'd rather we run it.
Common questions about Robot Ops.
What robot log formats does Robot Ops work with?
Does the triage step call an AI API live?
Is Robot Ops a product or a service?
How does Robot Ops relate to the Physical AI Data Flywheel?
Point Robot Ops at your fleet's logs.
Robot Ops is deployed with a US robotics operator today, and we're taking on a small number of additional teams. If your robots are generating logs you're not learning from, we'd like to talk.