Every episode your robots run, on one timeline
Joint torque, gripper cameras, depth and transforms from the whole fleet — captured losslessly, searchable in seconds, and ready to train on.
Proprioception and vision, finally in one place
Phloem keeps every modality of an episode on one playhead, so debugging a grasp takes minutes, not an afternoon of file juggling.
Scrub the whole episode
Torque, current, wrist camera and depth snap to the same instant. See exactly what the policy saw when the grasp slipped.
Find the failure fleet-wide
Query every episode for the same slip signature. One anomaly becomes a labelled set of every occurrence.
Curate training data from real runs
Tag episodes, filter by outcome, and export LeRobot-format datasets straight from the timeline.
One platform, first prototype to fleet
The record built in development carries into validation and operations — nothing starts from scratch.
Develop
- Record episodes at the bench, network or not — they land on one timeline and sync when connected.
- Compare controller revisions run against run, joint by joint.
- Keep sim rollouts and real episodes side by side.
Validate
- Run policy evals as repeatable suites, scored on every revision.
- Codify pass criteria once; every future episode is checked automatically.
- Build edge-case libraries from real failures for regression.
Operate
- Capture the deployed fleet continuously, at full rate.
- Trend joint wear and drift across units before they fail.
- Route anomalies to tickets with the full episode attached.
What a run looks like in Phloem
A fleet episode as it lands: proprioception, wrist camera, depth and transforms, already on one playhead.
The scene, not just signalsDepth, detections and masks sit over the camera frame, with the point cloud beside them — an episode reads as a scene, not a folder of files.
Native to the robot stack
First-class ingest for the formats, protocols and buses this work already runs on — and the wider ecosystem behind them.
+ anything else with an SDK, a topic, or a recorded log.
What teams get back
Overnight fleet episodes arrive pre-correlated, so morning review starts at the anomalies instead of at the file system.
Curated episodes flow from the fleet into training-ready datasets on a schedule, not as a quarterly project.
Robots that learn from every episode
Bring the whole fleet’s data into one place, and turn every grasp — landed or missed — into the next policy.