FactoryNet4MIT
FactoryNet
A multi-embodiment industrial time-series corpus containing 56,591 task executions (14,847 real, 41,744 simulated) across 7 robot embodiments with 113M logged timesteps, structured in a unified Setpoint-Effort-Feedback-Context schema for anomaly detection and predictive maintenance.
Downloads37
Episodes56,591
Why This Matters for Physical AI
FactoryNet enables sim-to-real transfer and cross-embodiment anomaly detection by providing a unified control-theoretic schema (S-E-F-C) that standardizes sensor and command data across diverse industrial robots, addressing the critical challenge of learning generalizable dynamics models across different platforms.
Technical Profile
- Modalities
- time-seriesjoint_positionsjoint_velocitiesjoint_torquemotor_currenttcp_poseaccelerometercontact_force
- Robot Embodiments
- UR3UR5UR10UR30KUKA KR10CNC gantry
- Action Space
- joint_positions
- Environment
- labsimulation
- Task Types
- pick_and_placescrewingpeg_insertionmachining
- Episodes
- 56,591
- Data Format
- parquet
- Annotation Types
- anomaly_labelsfault_labelstask_phasecounterfactual_pairs
- License
- MIT
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