Hai Robotics Enhances Modular Goods-to-Person Automation System

Warehousing, e-commerce fulfilment, and distribution operations increasingly require scalable automation that balances throughput, storage density, and system simplicity. At LogiMAT 2026 in Stuttgart (24–26 March), Hai Robotics presented an upgraded version of its HaiPick Climb goods-to-person automation system, expanding modular capabilities while maintaining its established operational architecture.

Transition from deployment to scalable automation
Initially introduced the previous year, HaiPick Climb has progressed from a demonstration system to active deployment across multiple regions. Thousands of HaiClimber robots are currently supporting daily warehouse operations, including picking, replenishment, and order fulfilment tasks.

The system has also been adopted within broader automation ecosystems through integrators such as Dematic, Körber, Fortna, and Vanderlande, indicating its compatibility with existing warehouse management and control systems. This integration capability is critical for facilities aiming to scale automation without replacing entire infrastructures.

Modular upgrades for storage density and flexibility
The updated HaiPick Climb system introduces additional configuration options while retaining its core design principle of simplified automation. A key enhancement is support for double-deep storage, enabling higher storage density without expanding warehouse footprint—an important factor in high-cost or space-constrained facilities.

The system now also accommodates a broader range of load types, including totes, cartons, and products stored in original packaging. This flexibility allows operators to handle mixed inventory profiles without requiring standardized containers, which can reduce preprocessing steps and associated costs.

Workstation configurations have also been expanded, allowing operators to tailor picking and replenishment stations to specific throughput requirements. These modular adjustments enable the system to align more closely with different operational strategies, from high-volume e-commerce picking to slower-moving industrial inventory handling.

Maintaining simplified system architecture
Despite the expanded functionality, the system maintains a consistent architecture based on vertical climbing robots operating within racking structures. This design reduces the need for complex conveyor networks or large autonomous vehicle fleets, which can increase system cost and integration complexity.

The use of modular components allows warehouses to incrementally expand system capabilities—such as increasing storage depth or adding workstations—without redesigning the entire installation. This approach supports phased automation strategies and reduces disruption during system upgrades.

Positioning within goods-to-person automation
Goods-to-person automation systems are widely used to improve picking efficiency and reduce operator travel time. Comparable solutions are offered by companies such as AutoStore and Geek+, which also focus on high-density storage and robotic retrieval.

However, HaiPick Climb differentiates itself through its climbing robot architecture, which operates directly on storage racks rather than relying on grid-based systems or floor-based mobile robots. This can simplify deployment in certain warehouse layouts and reduce infrastructure requirements, particularly in retrofit scenarios.

Live demonstration at LogiMAT
At Booth 3A03, Hai Robotics demonstrated the upgraded HaiPick Climb system in operation, highlighting how modular configurations can be adapted to different warehouse environments. The presentation focused on practical implementation aspects, including system scalability, integration with existing workflows, and adaptability to changing inventory profiles.

By building on an already deployed system and extending its modularity, Hai Robotics positioned HaiPick Climb as a configurable goods-to-person automation solution suited for warehouses seeking to increase efficiency without introducing excessive system complexity.

Edited by Natania Lyngdoh, Induportals Editor — Adapted by AI.

www.hairobotics.com