CLARU
paulpacaudapache-2.0

Guardian Failure Detection Dataset

An annotated robotic manipulation failure dataset for training Vision-Language Models on planning and execution error detection, containing procedurally generated diverse failure cases from RLBench, BridgeDataV2, UR5, and RoboFail sources.

Downloads78
Episodes32,619

Why This Matters for Physical AI

This dataset enables vision-language models to detect and diagnose robotic planning and execution failures, which is critical for improving safety, reliability, and autonomous error recovery in real-world robotic systems.

Technical Profile

Modalities
rgblanguage
Robot Embodiments
UR5gripper
Environment
simulationlab
Task Types
manipulationgraspingpick_and_place
Episodes
32,619
Data Format
JSONL
Annotation Types
language_instructionsfailure_labelsfailure_categoriesaction_labelsreward_labels
License
apache-2.0
Part of the Guardian family

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