liveplex/robogate-failure-dictionary

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

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)

Usage

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

Research Foundations

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 size is not the bottleneck — even NVIDIA's flagship 3B model cannot bridge the training-deployment distribution gap.

Leaderboard: robogate.io/vla · Paper: arXiv:2603.22126

Citation

@dataset{robogate_failure_dictionary_2026,
  title={RoboGate Failure Dictionary: 50K+ Physics-Validated Pick & Place Failure Patterns},
  author={RoboGate Team},
  year={2026},
  url={https://huggingface.co/datasets/liveplex/robogate-failure-dictionary},
  note={Franka Panda + UR3e + UR5e + UR10e, Two-Stage Adaptive Sampling, AUC 0.777}
}

Links

• —GitHub: liveplex-cpu/robogate-failure-dictionary
• —RoboGate Platform: liveplex-cpu/robogate