Failure ingestion

The loop starts with what your robots actually do. You send rollouts, including the failures and the interventions. Avala reads them, finds the patterns and tells you what data is missing.

Formats

Format	How it arrives
MCAP	Fleet recording upload, or `avala import folder --data-type mcap`
ROS bags (`.bag`, `.db3`)	`avala import rosbag`, or convert to MCAP and upload as a fleet recording
LeRobot datasets	`avala import lerobot` from the Hugging Face Hub or a local directory
Video with logs	Video through `avala import folder --data-type video`. Avala aligns the logs with the video as part of the program.
Inspect Robots eval logs	`avala import inspect-robots`. See Import evaluation logs.

MCAP keeps every topic, so it gives the most complete picture. The ROS bag importer converts camera topics (sensor_msgs/Image and sensor_msgs/CompressedImage) and carries joint states and other numeric or text topics. Point clouds and other binary payloads are skipped. Send MCAP when you need every topic preserved.

Send rollouts as fleet recordings

Each upload goes into an existing fleet recording. Avala provisions recordings for your registered devices when the program starts. List them to find the recording UID.

from avala import Client

client = Client()  # reads AVALA_API_KEY

page = client.fleet.recordings.list(device_id="<device-uid>", limit=20)
for recording in page:
    print(recording.uid, recording.status, recording.started_at)

# Upload a local directory of rollout files into one recording.
session = client.fleet.uploads.upload_recording(
    "<recording-uid>",
    "./rollouts/2026-09-29",
)
print(session.uid, session.status)

# Preview what will be uploaded
avala fleet recordings upload \
  --source ./rollouts/2026-09-29 \
  --recording <recording-uid> \
  --dry-run

# Upload and wait for server-side processing
avala fleet recordings upload \
  --source ./rollouts/2026-09-29 \
  --recording <recording-uid> \
  --wait

upload_recording resumes an interrupted upload of the same source. A completed call means processing is queued. Check client.fleet.recordings.get(uid).status until it reads ready.

Mark failures and interventions as events

Fleet events put a label on a moment in a recording. Mark each failure and each intervention so Avala can find it fast. Use type for the event class and metadata for anything your stack logs, such as the model version and the task.

from avala import Client

client = Client()

event = client.fleet.events.create(
    recording="<recording-uid>",
    device="<device-uid>",
    type="anomaly",
    label="Plate slipped during placement",
    timestamp="2026-09-29T14:03:12Z",
    severity="warning",
    duration_ms=1800,
    tags=["failure", "regrasp"],
    metadata={"model_version": "your-policy@2026-09-20", "task": "load_dishwasher"},
)
print(event.uid)

# Many events at once
created = client.fleet.events.create_batch(
    events=[
        {
            "recording": "<recording-uid>",
            "device": "<device-uid>",
            "type": "state_change",
            "label": "Operator took over",
            "timestamp": "2026-09-29T14:03:15Z",
            "tags": ["intervention"],
        },
    ]
)
print(created)

import Avala from "@avala-ai/sdk";

const avala = new Avala(); // reads AVALA_API_KEY

const event = await avala.fleet.events.create({
  recording: "<recording-uid>",
  device: "<device-uid>",
  type: "anomaly",
  label: "Plate slipped during placement",
  timestamp: "2026-09-29T14:03:12Z",
  severity: "warning",
  durationMs: 1800,
  tags: ["failure", "regrasp"],
  metadata: { model_version: "your-policy@2026-09-20", task: "load_dishwasher" },
});
console.log(event.uid);

avala fleet events create \
  --recording <recording-uid> \
  --device <device-uid> \
  --type anomaly \
  --label "Plate slipped during placement" \
  --timestamp 2026-09-29T14:03:12Z \
  --severity warning

Event type is one of error, warning, info, state_change, anomaly or custom. severity is one of info, warning, error or critical. You can also create events automatically with recording rules.

Import ROS bags and LeRobot datasets

When rollouts live outside the fleet, import them as a dataset. Install the extra for the format first.

pip install "avala[rosbag]"
avala import rosbag ./rollout_0412.bag --name "Rollouts 0412" --slug rollouts-0412 --wait

pip install "avala[lerobot]"
avala import lerobot --repo-id your-hf-user/your-rollouts --name "Rollouts" --slug rollouts --wait

The LeRobot importer is described in detail in Exports.

Import evaluation logs

If you evaluate your policy with Inspect Robots, an open-source evaluation tool, import its JSON eval log. Each trial, one scene in one epoch, labels one sequence with an outcome label. Failed trials become sequences you can filter, review and turn into collection work.

pip install "avala[inspect]"

# Preview the trial-to-outcome mapping. Reads the log only.
avala import inspect-robots logs/load-dishwasher_1a2b3c4d.json --dataset your-org/dishwasher-eval --dry-run

# Label the sequences that already exist in the dataset, one per trial
avala import inspect-robots logs/load-dishwasher_1a2b3c4d.json --dataset your-org/dishwasher-eval

# Or create the dataset from each trial's recorded actions, plus camera frames if the run stored them
avala import inspect-robots logs/load-dishwasher_1a2b3c4d.json --dataset your-org/dishwasher-eval --create

Inspect Robots trial	Outcome
Success score at or above `--success-threshold`, grading score at or above `--expert-threshold`	`expert_success`
Success score at or above `--success-threshold`, grading score below `--expert-threshold`	`partial_success`
Success score below `--success-threshold`	`failure`
Trial errored or was cancelled	`aborted`
Success scorer produced no value	Not labeled

Every imported label has source set to imported, evaluation_membership set to held_out_eval and autonomy_level set to autonomous. model_version is the run’s policy. The run’s task becomes one subtask that spans the trial. Labels written by a person or a model are kept unless you pass --overwrite. The full option list is in the CLI reference.

Inspect recordings and events with the MCP server

The MCP server reads fleet data. Ask your assistant about recent failures, or call the tools directly:

fleet_list_recordings and fleet_get_recording for recordings
fleet_list_events filtered by recording, device, type or severity
get_fleet_health for a fleet-wide summary

Uploads and event creation go through the SDK or the CLI. See Use Avala with AI to connect.

What Avala does with the rollouts

The steps below are a service Avala performs as part of the program. There is no API to call for them.

Segmentation. Avala splits each rollout into sequences and subtasks at state changes. Your events anchor the failure moments.
Labeling. Each sequence gets both axes of its sequence metadata: a technical validity check and an outcome label.
Clustering. Avala groups failures by what went wrong and under which conditions. Clusters map to a versioned failure taxonomy for your program. New clusters extend the taxonomy.
Gap analysis. Avala compares the failure clusters with the coverage of your existing training data and the evaluation cohorts.

The gap report

Avala delivers a gap report for each batch of rollouts.

Section	Contents
Scope	Recordings, time range, model versions and taxonomy version.
Failure taxonomy	Each failure mode, its definition and example sequences.
Distribution	Failure counts and rates per mode, per model version and per coverage cell.
Missing coverage	The coverage cells behind each failure mode that your training data lacks or holds too thinly.
Proposed experiments	One draft capability experiment per priority failure condition, with a hypothesis and target cells.

You review the gap report and choose priorities. The approved experiments become the next collection brief.

Getting Started

Fleet Learning

Platform

Use Cases

Formats

Send rollouts as fleet recordings

Mark failures and interventions as events

Import ROS bags and LeRobot datasets

Import evaluation logs

Inspect recordings and events with the MCP server

What Avala does with the rollouts

The gap report

​Formats

​Send rollouts as fleet recordings

​Mark failures and interventions as events

​Import ROS bags and LeRobot datasets

​Import evaluation logs

​Inspect recordings and events with the MCP server

​What Avala does with the rollouts

​The gap report

Formats

Send rollouts as fleet recordings

Mark failures and interventions as events

Import ROS bags and LeRobot datasets

Import evaluation logs

Inspect recordings and events with the MCP server

What Avala does with the rollouts

The gap report