lerobot-lint 0.1.0

Deterministic, rule-based checks that catch behavioral/kinematic data bugs in LeRobot robot-training datasets before you burn GPU-hours training on bad data.

lerobot-lint

Your loss curve looks perfect but the robot pecks at the table. The bug might be in your data, not your model.

lerobot-lint is a CLI (lelint) and Python package (lerobot_lint) that checks LeRobot robot-training datasets for behavioral/kinematic data bugs — dead joints, encoder wraparound, frozen telemetry, leader/follower calibration mismatch, frame drops — before you burn GPU-hours training on bad data.

Deterministic, rule-based checks. No ML, no training, no GPU required to run it.

Status: works, pre-1.0, not on PyPI yet

20 checks across 5 groups are implemented and tested (130 passing tests). The CLI runs end-to-end against real Hugging Face Hub datasets — loading, checking, console/JSON reports, exit codes, a guard() context-manager API for use inside training scripts.

Not published to PyPI yet — install from source (below). See PROGRESS.md for exactly what's built, what's left, and why.

Known limitation: camera/video checks (frozen camera, degenerate image, video length mismatch) are implemented and unit-tested against synthetic frame data, but real video decoding isn't wired up yet — they don't see real camera frames from an actual dataset run today. Everything else (joint kinematics, action/state consistency, timing, dataset hygiene) runs against real data now.

Install (from source)

git clone https://github.com/mannasdev/lerobot-lint.git
cd lerobot-lint
pip install -e .

Use it

lelint check lerobot/some-dataset --no-video

Auto-detects a robot profile (so101/koch joint-naming convention) from the dataset's own metadata when --profile isn't passed, and always tells you which profile it used and why — never silently. --fail-on info|warning|error controls what makes the exit code nonzero (default: error, exit 0/1/2). --json report.json writes a machine-readable report alongside the console one.

import lerobot_lint

with lerobot_lint.guard("your/dataset"):
    ...  # raises before you spend GPU-hours on data with real errors

A real example

Running it against lerobot/svla_so101_pickplace (a real SO-101 pick-place dataset) surfaces genuine recorder dropouts undisclosed in the dataset card — 5 of 50 episodes have stretches where the robot's telemetry is bit-identical for up to 2+ seconds while actively recording (verified by hand against the raw data, not just this tool's own output):

$ lelint check lerobot/svla_so101_pickplace --no-video
Using profile: koch (auto-detected from joint names, override with --profile)
...
Errors (...)
  [FROZEN_STATE] episode 0: State frozen (identical for 67 consecutive frames) from
  frame 27 to 93 -- likely a recorder hiccup or serial dropout

An imitation-learning policy trained on episode 0 as-is would learn "do nothing" for over a quarter of that episode's frames.

What's built

• All 20 checks across 5 groups (kinematic signal, action/state consistency, timing integrity, camera/video, dataset hygiene) — see lerobot_lint/checks/.
• The dataset loader, verified against real public LeRobot datasets (including real SO-101 hardware, not just simulated tasks).
• The check engine: two-pass streaming, per-check crash isolation, episode- and dataset-scoped checks.
• The lelint CLI: check (with --profile, --episodes, --no-video, --json, --verbose, --fail-on), profiles, version.
• lerobot_lint.guard() — a context-manager API for use inside training scripts.
• Console and JSON reports.
• Robot profile auto-detection from joint-naming convention.

What's not built yet

The A-F scorecard formula, the bug-card renderer, real video-decode wiring, PyPI packaging/CI, and the full field study (calibrating thresholds against 30-50 real datasets). Full list with context in PROGRESS.md.

License

Apache-2.0 — see LICENSE.