HaptalAI/robotics-failure-benchmark

Haptal Robotics Failure Benchmark v1.0

The first public benchmark for robot training data annotation quality and failure detection in manipulation episodes.

What this is

A held-out test set of 600 robot episodes across 6 failure classes generated from real LeRobot trajectories with physics-based failure injection. The test set is fixed. Anyone can evaluate their annotation pipeline against it and get a comparable score.

Why it exists

No standardized benchmark currently exists for robot training data annotation quality. Open X-Embodiment, BridgeData V2, DROID and LeRobot are dataset collections. None measure annotation quality or failure detection accuracy. We built this to fill that gap.

Failure classes

Class	Description
grasp_slip	Grip force drops causing object slip
nominal	Successful episode, no failure
overcorrect	Post-failure panic response
stuck_joint	Motor stall or joint lock
trajectory_deviation	Drift from intended path
velocity_spike	Sudden joint velocity anomaly

Dataset splits

Split	Episodes
Train	2,400
Test (fixed)	600

Leaderboard

Rank	Model	Accuracy	Macro F1	Cohen's κ	OOD F1	Gap
🥇	Haptal (multi-dataset RF)	93.6%	0.937	0.923	0.907	0.030
—	Human operator (pass/fail only)*	83.1%	—	0.661	—	—
—	Majority baseline	53.1%	—	0.000	—	—

* Human operators provide binary pass/fail only — no failure type, no timestep.
Submit your model → aarav@haptal.ai · see leaderboard.json for full entry details.

Haptal baseline results

Metric	Value
In-distribution accuracy	93.6%
OOD accuracy (unseen robot)	90.8%
Generalization gap	0.03
Macro F1	0.937
Cohen's Kappa	0.923
Weakest class F1	0.887 (grasp_slip)

How to use

from datasets import load_dataset

dataset = load_dataset("haptal-ai/robotics-failure-benchmark")
train = dataset["train"]
test  = dataset["test"]

How to score your model

1. Run your model on the test split
2. Save predictions as a CSV with columns episode_id and predicted_class
3. Run python score.py your_predictions.csv
4. Email results to aarav@haptal.ai to be added to the leaderboard

Base datasets

Generated from real robot trajectories across multiple platforms:

• lerobot/pusht
• lerobot/xarm_lift_medium_replay
• lerobot/xarm_push_medium_replay
• lerobot/aloha_sim_transfer_cube_human

License

Apache 2.0. Base trajectories from LeRobot (MIT license).

Citation

@dataset{haptal2026rfb,
  title   = {Haptal Robotics Failure Benchmark v1.0},
  author  = {Bedi, Aarav},
  year    = {2026},
  publisher = {HuggingFace},
  url     = {https://huggingface.co/datasets/haptal-ai/robotics-failure-benchmark}
}

Contact

aarav@haptal.ai
haptal.ai