Assembly & Tool-Use Data for Manufacturing Robots
Assembly, fastening and tool-use demonstrations with action phases and sequence-quality labels for industrial robot learning. Select procedures with Shaip.
Dataset Specifications
| Target Buyer Teams | Industrial Robotics Lead; Manufacturing AI Director; Automation Engineering; Robot Learning Team |
| Representative Tasks | Fastening, part insertion, inspection, measurement, tool changes and workcell procedures |
| Capture / Modalities | Egocentric/exocentric RGB-D; optional hand pose, tool tracking, IMU and wearable sensors |
| Annotations / Metadata | Task steps, tool/object identity, contact, sequence errors, completion quality and safety states |
| Delivery Formats | MP4 + JSON/Parquet + optional CAD/object metadata and calibration |
| Scale (Illustrative)* | 500–2,500 demonstration hours |
| Participants / Operators | 100–400 operators |
| Tasks / Scenarios | 50–200 procedures |
| Objects / Sites / Views | 100–500 tools/components |
| Recommended Engagement Scope | 75–200 hours across 10–25 procedures and 25–75 components |
Supported by Shaip's task-based capture and industrial-object annotation operations.
View the related case study →Where This Dataset Is Used
This industrial assembly dataset is built for teams training manufacturing robots and industrial VLA models on assembly, fastening, inspection and tool-use procedures. Industrial automation companies, robot OEMs and manufacturing AI teams use this robot tool use dataset as an assembly task dataset for skill learning, procedure understanding and sequence-quality evaluation. If you’re sourcing manufacturing robotics data, a procedural manipulation dataset or embodied AI assembly data with action phases and quality labels, this dataset supports robot learning from electronics assembly to heavy fastening.
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