Warehouse & Logistics Robotics Data
Training data for warehouse robots: AMRs, pick-and-place, sortation, and inventory management in fulfillment centers and distribution hubs.
Regulatory Requirements
OSHA Standards (United States)
Occupational Safety and Health Administration warehouse safety requirements. Training data for warehouse robots operating near humans must include near-miss detection and safety zone violation examples for safety-critical model training.
ANSI/RIA R15.08 (United States)
Industrial Mobile Robot Safety standard for AMRs. Data must cover the dynamic obstacle scenarios and human interaction patterns specified in R15.08 safety validation requirements.
Environment Characteristics
High-Density Racking
Narrow aisles with floor-to-ceiling shelving and varying product densities. Data challenge: Limited field of view in narrow aisles, repetitive visual patterns across shelf bays, and dynamic shelf occupancy.
Mixed Human-Robot Zones
Warehouse workers and robots sharing operational space. Data challenge: Robust person detection under varying lighting, partial occlusion by carts and equipment, and diverse worker appearances.
Variable Lighting
Warehouse lighting ranges from bright loading docks to dim deep-rack areas. Data challenge: Models must handle 10-1000 lux range within a single navigation path.
Common Robotics Tasks
Autonomous Navigation
Safe navigation of AMRs through warehouse aisles with dynamic obstacles. Data requirements: Egocentric video from robot height, LiDAR scans, floor-plan maps, dynamic obstacle trajectories, and pedestrian intent prediction data.
Pick-and-Place
Grasping diverse products from bins and shelves for order fulfillment. Data requirements: Multi-view product images, depth data for bin picking, grasp trajectory recordings across product categories.
Inventory Counting
Autonomous shelf scanning for real-time inventory tracking. Data requirements: Shelf-facing images with product detection annotations, barcode readability labels, and empty-slot detection.
Sortation
High-speed package sorting by destination zip code, carrier, or priority. Data requirements: Conveyor-view video with package tracking, label OCR ground truth, and destination classification labels.
Relevant Data Modalities
RGB video, LiDAR, Depth, IMU, Barcode/OCR are the primary data modalities for warehouse & logistics robotics data. Each modality captures different aspects of the warehouse logistics environment, and the optimal sensor mix depends on the specific robotic application.
Key References
- [1]Duan et al.. “A Survey on Autonomous Mobile Robot Navigation in Warehouses.” Robotics and Autonomous Systems 2023, 2023. Link
- [2]Mahler et al.. “Learning Ambidextrous Robot Grasping Policies.” Science Robotics 2019, 2019. Link
- [3]Correll et al.. “Analysis and Review of the State of Warehouse Robotics.” Frontiers in Robotics and AI 2022, 2022. Link
How Claru Serves This Industry
Claru captures data in active fulfillment centers during normal operations. Our warehouse collection covers major facility configurations (goods-to-person, person-to-goods, shuttle systems) with real product diversity and authentic worker movement patterns.
Frequently Asked Questions
Yes. All collection occurs in active warehouses, capturing authentic operations, real product diversity, and genuine human-robot interaction patterns.
The data is platform-agnostic. Navigation data includes floor-level and elevated viewpoints matching common AMR sensor heights from 0.3m to 1.5m.
Yes. The dataset includes annotated near-miss events, safety zone violations, and human-present navigation scenarios required for safety-critical model validation.
Thousands of real SKUs: boxes, polybags, bottles, blister packs, and irregular items ranging from 50g to 25kg.
Yes. Custom collection campaigns in client warehouses are available with NDA protection and custom annotation requirements.
Discuss Warehouse & Logistics Robotics Data Needs
Tell us about your warehouse logistics project. Claru will scope a data collection and annotation plan tailored to your requirements.