| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| - tabular-classification |
| tags: |
| - robotics |
| - failure-detection |
| - robot-training-data |
| - annotation-quality |
| - manipulation |
| pretty_name: Haptal Robotics Failure Benchmark v1.0 |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # 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 |
| |
| ```python |
| 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](mailto: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 |
|
|
| ```bibtex |
| @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](mailto:aarav@haptal.ai) |
| [haptal.ai](https://haptal.ai) |
|
|
