Pick-and-Place Data Across 1,000+ SKUs
Warehouse picking, packing and sorting demonstrations across 100–1,000 SKUs with grasp, failure and clutter labels for picking policies. Scope a warehouse program with Shaip.
Dataset Specifications
| Target Buyer Teams | Warehouse Robotics Director; Manipulation Lead; Automation Product Team; AI Data Lead |
| Representative Tasks | Bin picking, shelf picking, tote transfer, packing, sorting, scanning and recovery |
| Capture / Modalities | Multi-view RGB/RGB-D; optional human demonstrations, teleoperation, robot states and gripper data |
| Annotations / Metadata | Object IDs, grasp phase, pick/place coordinates, success, failure reason, clutter and occlusion |
| Delivery Formats | MP4/RGB-D + JSON; LeRobot/RLDS/HDF5 only for synchronized robot episodes |
| Scale (Illustrative)* | 500–3,000 demonstration hours |
| Participants / Operators | 100–500 operators |
| Tasks / Scenarios | 20–100 task variants |
| Objects / Sites / Views | 100–1,000 SKUs |
| Recommended Engagement Scope | 100–300 hours across 100–250 SKUs and 10–20 task variants |
Aligned with Shaip's existing humanoid, warehouse-task and multimodal data-operations positioning.
View the related case study →Where This Dataset Is Used
This warehouse robotics dataset is built for teams training picking policies, bin picking systems and warehouse automation robots across diverse SKUs and clutter conditions. Logistics automation companies, robot picking startups and fulfillment AI teams use this pick and place dataset as a robotic picking dataset for grasp learning, failure recovery and SKU generalization. If you’re sourcing warehouse automation training data, bin picking data or a manipulation demonstration dataset with grasp, failure and clutter labels across 100–1,000 SKUs, this dataset supports policy training from pilot cell to live fulfillment.
Request a Sample of the Warehouse Pick-and-Place Demonstration Dataset
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