Piece Picking Robots

Global Piece Picking Robots Market to Reach US$13.7 Billion by 2030

The global market for Piece Picking Robots estimated at US$1.1 Billion in the year 2024, is expected to reach US$13.7 Billion by 2030, growing at a CAGR of 52.3% over the analysis period 2024-2030. Collaborative Robot, one of the segments analyzed in the report, is expected to record a 57.0% CAGR and reach US$9.9 Billion by the end of the analysis period. Growth in the Mobile & Other Robots segment is estimated at 43.4% CAGR over the analysis period.

The U.S. Market is Estimated at US$298.3 Million While China is Forecast to Grow at 63.8% CAGR

The Piece Picking Robots market in the U.S. is estimated at US$298.3 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$3.6 Billion by the year 2030 trailing a CAGR of 63.8% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 43.5% and 49.3% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 46.3% CAGR.

Global Piece Picking Robots Market - Key Trends & Drivers Summarized

Why Are Piece Picking Robots Becoming Indispensable in Warehouse Automation?

Piece picking robots, designed to identify, grasp, and move individual items from shelves or bins to order containers, are transforming fulfillment centers and distribution hubs by automating one of the most labor-intensive and error-prone tasks in warehouse operations. As e-commerce order volumes and SKU diversity explode, manual picking processes are proving inefficient, costly, and unsustainable. Robots offer the precision, scalability, and round-the-clock operability needed to meet next-day delivery expectations.

These robots leverage computer vision, machine learning, and robotic grippers to accurately identify a wide range of objects with varying sizes, shapes, and packaging types. Unlike traditional industrial robots designed for repetitive motion, piece picking systems are engineered for unstructured environments and must respond dynamically to real-time input. This capability makes them particularly well-suited for retail warehouses, pharmaceutical distribution, and third-party logistics (3PL) operations where order profiles vary by customer and season.

What differentiates modern piece picking robots is their adaptability. Rather than relying on predefined programs or rigid SKU arrangements, they are taught using data. Their ability to learn from past attempts, recognize patterns, and self-correct makes them highly flexible in fast-moving warehouse environments. This evolution from fixed automation to intelligent, perception-driven robotics is changing the economics of fulfillment.

How Are Technological Advancements Enhancing Performance and Reliability?

Piece picking robots are now equipped with sophisticated vision systems, combining RGB-D cameras, 3D scanners, and AI algorithms that enable object recognition even when items are partially occluded or overlapping. Deep learning models allow these robots to continually improve their accuracy in classifying products, identifying optimal grasp points, and adjusting suction or gripping force dynamically based on material feedback.

End-effectors have also evolved-from single suction cups to adaptive grippers that use soft robotics and tactile sensors. These multi-modal grippers can handle everything from rigid boxes to soft polybags and fragile containers. Integration of force sensors and real-time torque control ensures gentle yet secure handling, which is critical for pharmaceuticals, electronics, and perishables.

Edge computing and cloud robotics are enabling real-time decision-making with minimal latency. Robots can now be deployed as part of collaborative systems, working alongside human operators or other autonomous mobile robots (AMRs) to streamline inventory movement. Some platforms offer vision-as-a-service, where robots are updated via cloud-hosted AI models, ensuring consistency across fleets and continuous improvement.

Which Sectors and Warehouse Types Are Leading Adoption of Piece Picking Robots?

E-commerce and retail sectors are leading the adoption wave, driven by high SKU complexity and customer demand for rapid order fulfillment. Piece picking robots are deployed extensively in grocery micro-fulfillment centers, apparel warehouses, and electronics distribution hubs, where SKU counts often exceed tens of thousands. Their ability to scale operations without requiring linear increases in labor is a compelling proposition.

3PL providers and logistics companies are also embracing robotic picking to increase throughput while minimizing reliance on seasonal or temporary labor. During peak shopping seasons, robotic systems provide consistency and resilience against workforce shortages. Pharmaceutical companies are deploying them in GMP-compliant warehouses to handle serialized medications with traceability and precision.

Adoption is also gaining ground in manufacturing for kitting operations and just-in-time component delivery. As warehouses move toward dark store models-highly automated hubs without human intervention-piece picking robots will serve as a linchpin for end-to-end automation. Geographically, North America and Europe are early adopters, with APAC markets like China, Japan, and South Korea rapidly closing the gap due to aggressive automation investment.

What Is Driving Growth in the Global Piece Picking Robots Market?

The growth in the global piece picking robots market is driven by the surge in e-commerce fulfillment demand, labor shortages in warehousing, rapid improvements in vision and gripping technologies, and the need for scalable automation solutions in complex warehouse environments. With the average cost of picking constituting a major chunk of fulfillment expenses, robotic picking offers a direct path to cost reduction and operational agility.

Advancements in AI/ML, robotics-as-a-service (RaaS) models, and seamless API integration with WMS/ERP systems are further lowering barriers to adoption. Vendors are increasingly offering modular, plug-and-play systems that can be integrated with existing racking and conveyor infrastructure. The ROI profile is improving as robots achieve higher picks per hour and reduce damage and return rates.

Rising customer expectations for rapid, error-free delivery, coupled with warehouse space constraints, are driving the shift toward high-density, high-efficiency fulfillment systems. In this paradigm, piece picking robots play a pivotal role by bringing precision, adaptability, and data-driven learning into the heart of warehouse operations. As fulfillment complexity grows, the demand for autonomous, intelligent picking systems is poised for exponential growth.

SCOPE OF STUDY:

The report analyzes the Piece Picking Robots market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Robot Type (Collaborative Robot, Mobile & Other Robots); End-User (Pharmaceutical End-User , Retail / Warehousing / Distribution Centers / Logistics Centers End-User, Other End-Users)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

Select Competitors (Total 34 Featured) -

• ABB
• Bastian Solutions
• Covariant
• Daifuku
• Dexterity
• Epson Robots
• FANUC
• Honeywell International
• InVia Robotics
• KPI Solutions
• Locus Robotics
• Mujin
• Nomagic
• OSARO
• RightHand Robotics
• SSI?Schafer
• Swisslog (ItemPiQ)
• Symbotic
• Vecna Robotics
• Zivid

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