Warehouse robots move inventory in several different ways. Some bring shelves or pods to a worker, some carry carts and orders alongside human pickers, and robotic arms grasp individual products. AI and other software help connect orders to movement and, in item picking, help a machine perceive an object and act on it. The key distinction is that transporting inventory is not the same task as picking an item out of a bin.
What “warehouse robot picking” can mean
The phrase covers a set of workflows rather than one universal machine. A mobile robot may move a pod, tote, cart, or order through a building while a person does the physical selection. A robotic arm may instead reach into a container and grasp a specific item. Automated storage and retrieval equipment can also move inventory within a facility, and may be integrated with mobile robots or picking stations.
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These systems solve different parts of fulfillment. Transport automation reduces or reorganizes the movement of people and goods; robotic item picking automates the physical selection of an individual product. A warehouse may use more than one kind at once. The presence of robots does not mean every product is selected robotically, nor that every robot uses the same AI architecture.
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- Transport: move inventory, carts, or orders to or through a work area.
- Goods-to-person: deliver inventory-bearing storage units to a stationary associate.
- Item picking: perceive, select, and move an individual product, potentially with a robotic arm.
- Orchestration: assign and coordinate tasks across software, robots, and human work.
Keeping these jobs separate makes performance claims easier to interpret: a robot can improve how inventory reaches a person without itself grasping any product.
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How the main workflows operate
Goods-to-person: bring a pod or storage unit to the picker
In a goods-to-person setup, mobile drives retrieve inventory-bearing pods and bring them to a worker at a picking station. The associate identifies the needed item and takes the ordered quantity from the pod. The robot handles travel and positioning; the human performs the item selection in the workflow described by Amazon’s applied research.
Software has to decide which available pods should be sent to fulfill the current orders. That choice matters because the same item can be held in different places, and the selected pod determines how much robot travel is needed. Amazon’s 2023 INFORMS Journal on Applied Analytics article describes a redesigned pod-selection algorithm and uses drive distance per pick as a key efficiency measure. That is a particular algorithm applied to Amazon Robotics’ system, not a result that can be assumed for another warehouse.
For readers evaluating this design, the important question is not simply how many robots are present. It is how effectively the system selects and routes inventory-bearing units while keeping the station supplied with the products workers need.
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Autonomous mobile robots (AMRs) can navigate between locations and support people by carrying goods, carts, or orders. The worker may travel to a product location and pick it, while a robot carries the resulting goods or accompanies the work through a defined route. DHL describes its LocusBots as operating collaboratively with human workers in live fulfillment operations.
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This model changes the movement pattern rather than necessarily automating the grasp. A mobile robot can be part of the picking process even when a person still finds and handles every unit. Its contribution depends on the task assignment and facility workflow: a robot assigned to an order or cart has to move where that work requires, while associates perform the human portions of the process.
DHL Group reported that its Locus Robotics AMRs completed a cumulative 500 million picks, with the milestone pick occurring on May 18, 2024, at a DHL facility in Toledo, Spain. The figure is DHL’s company-reported milestone for its deployments; it is not a per-robot rate, a productivity comparison, or a forecast for another site.
Robotic item picking: select and grasp a product
When a robot arm picks an individual item, it must solve a more demanding physical problem than moving a pod or cart. Amazon director of robotic sortation technologies Julie Mitchell summarized the challenge in an Associated Press interview as three parts: “you need to perceive the scene, plan your motion and then execute.” These stages help explain why a system that moves reliably through a building may still be unable to pick every item on a shelf.
- Perceive: detect the object and relevant surroundings. A system needs enough information about where the item is and what is around it to choose an action.
- Plan: select a grasp or motion that can reach and handle the target in the observed scene.
- Execute: move the arm and gripper through the planned action, then determine whether the task was completed well enough for the workflow to continue.
Amazon’s official materials describe robotic arms and computer-vision-enabled systems, but that does not establish that one arm can handle every SKU, packaging type, or arrangement. An item’s shape, presentation, neighboring products, and the specific robot’s capabilities all matter. Nor does the description imply every warehouse uses an identical perception, planning, or control stack.
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In a 2024 Associated Press interview, Mitchell said that 70% of items shipped through the robotics system in one Nashville-area building during the deployment period she described went through that system. This is a facility-specific, Amazon-reported figure. It should not be read as a general share for Amazon warehouses or as evidence that 70% of all warehouse items can be robot-picked elsewhere.
Storage and retrieval can coexist with picking
Automated storage and retrieval systems move goods into, out of, or within storage infrastructure. They may feed a station or connect to mobile transport and picking equipment, but the storage movement itself is not proof that individual item selection is automated. AMRs, goods-to-person systems, storage and retrieval equipment, and robotic picking arms are distinct categories that can be combined in one facility. The available descriptions do not provide a complete engineering taxonomy, so these labels are most useful as a practical way to identify which part of the workflow a system handles.
How software connects robots to orders and people
Physical robots need software instructions that connect inventory and movement to fulfillment work. At a high level, the system must know what work is due, which inventory or route can serve it, and which robot or person is available to do the next task. A fleet-management layer can coordinate robot movements, while warehouse software connects those movements to order and inventory processes. The exact division of responsibilities varies by system; there is no single architecture established for every warehouse.
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When considering a deployment, ask how the robot’s task is created, how inventory and orders are represented in the connected systems, how changes in priority or availability are handled, and what the worker sees when an assignment changes. These are practical integration questions, not a claim that one software product or control design is standard across the industry.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the reported performance figures compare
The figures below describe different measures and evidence types. They are not comparable head-to-head tests: some count algorithm outcomes, some count completed picks, and others report customer-case results.
| Reported figure | What it describes | Source and qualification |
|---|---|---|
| 62% reduction in drive distance per unit picked; 31% reduction in the number of drives required; half a billion dollars in savings | Results reported for a redesigned pod-selection algorithm and its implementation | Authors of the 2023 INFORMS Journal on Applied Analytics article; Amazon Robotics implementation, not a general warehouse-robot guarantee |
| 500 million picks | Cumulative milestone for Locus Robotics AMRs at DHL | DHL Group, 2024; milestone pick on May 18, 2024, at its Toledo, Spain facility |
| More than 35 DHL locations | Reported deployment footprint of LocusBot operations | DHL Delivered coverage, 2024; time-bound company-reported count |
| Over 15% higher picking efficiency and 50% less employee travel time | Reported outcomes for a customer implementation | Zebra Technologies’ undated JAS Worldwide case study; vendor case-study figures, not independently verified or typical results |
| 70% of items shipped through the robotics system | Share reported for one building during a described deployment period | Amazon director Julie Mitchell in a 2024 Associated Press interview; one Nashville-area facility, Amazon-reported |
Use each number only for the scope it measures. A reduction in drive distance per picked unit is not the same as a reduction in labor, a count of picks is not a throughput rate, and a customer case study is not a controlled comparison across warehouses. The available figures do not establish independent, cross-vendor comparisons of total cost, uptime, safety, or throughput.
How to assess a warehouse robot system
Start with the physical job rather than the label “AI robot.” Identify what moves, who selects the item, and where the handoff occurs. Then examine whether the system’s software and reported metrics match the operation you want to improve.
- Name the handled unit. Is the system moving a pod, tote, cart, case, or individual item? A system designed around pods should not be described as an item-grasping robot unless it also performs that separate operation.
- Map the direction of work. Does inventory travel to a stationary worker, or does a person or robot travel to the inventory? Does a robot carry completed orders or carts after a person picks?
- Locate the human task. Record whether people identify, select, grasp, verify, or transfer the goods. “Collaborative” can describe a human-and-robot workflow without implying that the robot does the person’s part.
- Check the software handoffs. Ask how order priorities, inventory locations, robot availability, and task changes flow between warehouse software and the fleet system. Request specifics for the proposed implementation rather than assuming a case-study feature is universal.
- Match the metric to the goal. For travel reduction, seek a travel measure; for picking, seek a clearly defined picking measure. Establish its baseline, time period, facility scope, and whether the number comes from a vendor, customer, or independent measurement.
- Separate evidence from expectation. Published deployment claims can illustrate what happened in a named implementation, but they do not by themselves predict another site’s result. The sources summarized here do not supply apples-to-apples cost, uptime, safety, or throughput comparisons.
This approach prevents a category error: a robot that transports more inventory with less travel may be useful even if it never touches an individual product, while an arm that can grasp some items does not automatically solve broader fleet or order orchestration.
What AI does—and does not—tell you
“AI-powered” is not a sufficient description of a warehouse system. In one application, software may optimize which pod to send to a picking station. In another, computer vision and robotic control may support item perception and motion. Mobile robot navigation and task coordination add further software functions. These capabilities address different decisions and should be evaluated against the actual workflow.
A warehouse robot is therefore best understood as one component in an operating system that includes physical equipment, software, inventory information, orders, and human work. AI may contribute to a decision or perception task, but the label alone does not specify what the robot can carry, whether it can grasp a product, how it integrates with warehouse systems, or how well it performs in a particular facility.
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Which kind of robot is the right fit?
There is no universal “best warehouse robot” in the evidence available here. The appropriate category depends on the bottleneck. If the relevant work is bringing stored inventory to an associate, goods-to-person equipment addresses that transport-and-presentation job. If people pick at locations and need goods or orders carried, collaborative mobile robots address movement through the workflow. If the aim is to automate selection of individual objects, robotic item picking is the relevant capability—but it requires a system suited to the objects and scenes it will encounter.
A facility may combine these approaches rather than choose only one. Compare systems by the unit they handle, the path goods travel, who performs the pick, how tasks are coordinated, and what the evidence actually measures. That yields a more useful decision than treating every mobile robot or robotic arm as interchangeable.
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