| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - failure-analysis |
| - pick-and-place |
| - isaac-sim |
| - franka-panda |
| - ur5e |
| - ur3e |
| - ur10e |
| - domain-randomization |
| - latin-hypercube-sampling |
| - adaptive-sampling |
| - physical-ai |
| size_categories: |
| - 10K<n<100K |
| language: |
| - en |
| - ko |
| --- |
| |
| # RoboGate Failure Dictionary |
|
|
| > **50,000+ Physics-Validated Pick & Place Failure Patterns across 4 Robots (Franka Panda, UR5e, UR3e, UR10e)** |
|
|
| A structured database of robot AI failure patterns collected from **NVIDIA Isaac Sim** physical simulations using **Two-Stage Adaptive Sampling**. Each experiment records the exact conditions under which a robot succeeded or failed at Pick & Place tasks. |
|
|
| ## Quick Stats |
|
|
| | | Franka Uniform | Franka Boundary | UR5e | UR3e | UR10e | Combined | |
| |---|---|---|---|---|---|---| |
| | **Experiments** | 10,000 | 10,000 | 10,000 | 10,000 | 10,000 | **50,000+** | |
| | **Success Rate** | 33.3% | 63.8% | 74.3% | 10.0% | 0.0% | — | |
| | **Franka Combined** | — | 48.6% | — | — | — | — | |
| | **Risk Model AUC** | 0.65 | **0.777** | — | — | — | 0.777 | |
| | **Sampling** | Uniform LHS | Boundary LHS | Uniform LHS | Uniform LHS | Uniform LHS | Two-Stage | |
|
|
| ## Key Findings |
|
|
| - **friction × mass interaction z = -10.00** — strongest predictor of failure |
| - **Friction threshold: 0.492 ± 0.031** — below this, failure cascades |
| - **Mass > 0.93 kg** → Both robots fail at **< 40%** SR (universal danger zone) |
| - **Boundary equation:** μ*(m) = (1.469 + 0.419m) / (3.691 - 1.400m) |
| - **AUC improved 0.65 → 0.777** (+19.5%) with boundary-focused sampling |
| - **Failure mode transition:** friction↓ → timeout → collision → grasp_miss |
| |
| ## Two-Stage Adaptive Sampling |
| |
| **Stage 1 — Uniform Exploration (40,000)** |
| - Franka Panda 10K + UR5e 10K |
| - Latin Hypercube Sampling for uniform parameter space coverage |
| - Identified boundary regions and initial risk model (AUC 0.65) |
| |
| **Stage 2 — Boundary-Focused (10,000)** |
| - Franka Panda only, targeting boundary/transition regions |
| - Concentrated sampling near friction threshold 0.492 |
| - Revealed failure mode transitions invisible to uniform sampling |
| - Boosted Risk Model AUC to 0.777 (+19.5%) |
| |
| ## Universal Danger Zones (mass > 0.93 kg) |
| |
| | Mass Range | Franka SR | UR5e SR | |
| |---|---|---| |
| | 0.93 – 1.23 kg | 21.4% | 30.9% | |
| | 1.23 – 1.52 kg | 14.9% | 25.3% | |
| | 1.52 – 1.82 kg | 12.5% | 28.9% | |
| | 1.82 – 2.11 kg | 6.6% | 28.1% | |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("liveplex/robogate-failure-dictionary") |
| print(ds["train"][0]) |
| |
| # Filter danger zones |
| danger = ds["train"].filter(lambda x: x["zone"] == "danger") |
| print(f"Danger zones: {len(danger)}") |
| ``` |
| |
| ## Parameter Space |
| |
| | Parameter | Range | Scale | Paper | |
| |-----------|-------|-------|-------| |
| | friction | 0.05 – 1.2 | log-uniform | SIMPLER 2024 | |
| | mass | 0.05 – 2.0 kg | log-uniform | SIMPLER 2024 | |
| | com_offset | 0.0 – 0.40 | uniform | Suction Grasp 2025 | |
| | size | 0.02 – 0.12 m | uniform | SIMPLER 2024 | |
| | ik_noise | 0.0 – 0.04 rad | uniform | ICRA Sim2Real 2025 | |
| | obstacles | 0 – 4 | integer | RoboFAC 2025 | |
| | shape | 5 types | categorical | Grasp Anything 2024 | |
| | placement | 14 types | categorical | ALEAS 2025 | |
| |
| ## Research Foundations |
| |
| | Design Choice | Paper | Year | |
| |---|---|---| |
| | Two-Stage Adaptive Sampling | ALEAS | 2025 | |
| | friction × mass interaction | SIMPLER | CoRL 2024 | |
| | Failure taxonomy | RoboFAC | NeurIPS 2025 | |
| | Cross-robot validation | RoboMIND | RSS 2025 | |
| | UR-specific failures | Guardian | ICRA 2025 | |
| | Confidence intervals | SureSim | Badithela 2025 | |
| | GPU simulation | Isaac Lab | NVIDIA 2025 | |
| | Grasp evaluation | Isaac Sim Grasping SDG | NVIDIA 2025 | |
| |
| ## VLA Benchmark — 4-Model Leaderboard |
| |
| Four VLA models evaluated on RoboGate's 68-scenario adversarial suite. **All scored 0% SR** — including NVIDIA's official GR00T N1.6. |
| |
| | Model | Params | SR | Confidence | Failure Pattern | |
| |-------|--------|-----|-----------|-----------------| |
| | Scripted Controller | — | **100%** (68/68) | 76/100 | — | |
| | **GR00T N1.6 (NVIDIA)** | 3B | 0% (0/68) | 1/100 | grasp_miss + collision | |
| | OpenVLA (Stanford + TRI) | 7B | 0% (0/68) | 27/100 | grasp_miss dominant, 0 collision | |
| | Octo-Base (UC Berkeley) | 93M | 0% (0/68) | 1/100 | grasp_miss 79%, collision 21% | |
| | Octo-Small (UC Berkeley) | 27M | 0% (0/68) | 1/100 | grasp_miss 79.4%, collision 20.6% | |
| |
| Model size is not the bottleneck — even NVIDIA's flagship 3B model cannot bridge the training-deployment distribution gap. |
| |
| **Leaderboard:** [robogate.io/vla](https://robogate.io/vla) · **Paper:** [arXiv:2603.22126](https://arxiv.org/abs/2603.22126) |
| |
| ## Citation |
| |
| ```bibtex |
| @dataset{robogate_failure_dictionary_2026, |
| title={RoboGate Failure Dictionary: 30K Physics-Validated Pick & Place Failure Patterns}, |
| author={RoboGate Team}, |
| year={2026}, |
| url={https://huggingface.co/datasets/liveplex/robogate-failure-dictionary}, |
| note={Franka Panda + UR5e, Two-Stage Adaptive Sampling, AUC 0.777} |
| } |
| ``` |
| |
| ## Links |
| |
| - **GitHub**: [liveplex-cpu/robogate-failure-dictionary](https://github.com/liveplex-cpu/robogate-failure-dictionary) |
| - **RoboGate Platform**: [liveplex-cpu/robogate](https://github.com/liveplex-cpu/robogate) |
| |
