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Warehouse robots pick and move inventory in different ways: some bring storage pods or totes to a person, some carry goods or orders while people do the picking, and robotic arms grasp individual items. AI and related control software help systems choose tasks, navigate, recognize objects, and plan actions—but a robot that transports inventory is not necessarily a robot that can pick an item from a shelf.
What “warehouse robot picking” can mean
The phrase covers several distinct jobs. A mobile robot may transport a pod, tote, cart, or order through a building. In a goods-to-person system, it brings inventory to a stationary associate, who selects the units. In collaborative mobile-robot workflows, the machine supports a human picker by carrying goods or orders. A robotic arm, by contrast, attempts to grasp an individual item.
These approaches can coexist in one facility, alongside automated storage and retrieval equipment. They are not interchangeable: moving a loaded pod through a warehouse and recognizing, reaching for, and grasping one item are different technical tasks. “AI-powered” also does not describe a single standard architecture or mean that every robot independently makes every decision.
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Goods-to-person: bring the inventory to the worker
In a pod-to-picker workflow, mobile drives retrieve inventory-bearing pods and bring them to associates. The associate identifies and removes the units needed for an order; the robot’s role is to move the inventory, not necessarily to select the product. Amazon’s applied research describes an algorithm for choosing which inventory pods should be retrieved to fulfill orders. It measures efficiency in part by the drive distance per pick.
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This distinction matters when comparing systems. A mobile drive can automate substantial movement without performing item-level grasping. The human still carries out the selection step described in this workflow.
Collaborative mobile robots: carry goods or orders alongside people
Autonomous mobile robots, or AMRs, can navigate among warehouse locations and support human pickers by carrying goods, carts, or orders. DHL has described LocusBots working collaboratively with human employees in live fulfillment operations. In this arrangement, the robot assists with movement and coordination; that description does not establish that it grasps every item a worker picks.
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Robotic item picking: the arm selects an individual object
When an arm is expected to pick an individual item, it must handle a different set of challenges. Amazon robotics director Julie Mitchell described the core sequence in an Associated Press interview: “The challenge itself can be boiled down to three simple things: you need to perceive the scene, plan your motion and then execute.” The arm must interpret the scene, choose an approach, and carry out the movement. Performance on one task does not prove that an arm can handle every product, presentation, or warehouse condition.
Other automation can be part of the same facility
Automated storage and retrieval systems, goods-to-person systems, AMRs, and robotic picking arms are distinct categories in the reviewed overview. A warehouse may combine them, but the categories alone do not provide a complete engineering taxonomy or a fair, like-for-like comparison of systems.
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How an order moves through a robot-supported workflow
The exact process depends on the facility and its equipment. A useful way to understand a robot-supported order is to separate the work into these stages; this is a conceptual sequence, not a claim that every warehouse uses identical software or steps.
- Work is assigned. Orders create tasks, such as retrieving a pod, moving a tote, or supporting a pick. Warehouse and fleet software connect those tasks with robot and human work.
- The system selects what should move. In a pod-to-picker setup, the system must choose inventory-bearing pods that can contribute to order fulfillment. Amazon’s applied research focuses on this selection problem and the resulting drive distance per pick.
- A mobile robot transports the load, if the workflow uses one. Depending on the setup, it may bring a pod to an associate or carry goods, a cart, or an order through the building. Transporting a load is not the same as grasping an item from it.
- A person or a robotic arm selects the item. In goods-to-person and collaborative workflows, a person may pick. In item-level robotic picking, an arm has to perceive the scene, plan a motion, and execute it.
- Tasks are coordinated as conditions change. Fleet or warehouse software can assign and coordinate work. A DSV/Locus case-study directory describes dynamic task assignment and rerouting based on priorities and availability. That is a reported vendor/case-study description, not proof that every system works this way.
Where AI and control software fit
It is more accurate to ask what decision or action software supports than to assume a robot has one all-purpose AI brain. The available examples show several separate functions:
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- Choosing inventory to retrieve: Amazon’s pod-selection research describes selecting inventory pods for order fulfillment, with drive distance per pick as a key efficiency measure.
- Perception: For item-level picking, a system needs to interpret the scene around an object. Amazon’s official materials describe robotic arms and computer-vision-enabled systems, while Mitchell’s explanation identifies perception as the first part of the challenge.
- Motion planning and execution: After perceiving an item and its surroundings, a robotic arm must plan how to move and then execute that plan. The three stages are related but distinct; recognizing an item does not by itself make the grasp succeed.
- Task and fleet coordination: Warehouse or fleet software links robot assignments with orders and human work. The DSV/Locus description offers one example of reported dynamic assignment and rerouting, not a universal design specification.
These functions should not be collapsed into a claim that every warehouse robot uses the same AI, sees every product in the same way, or makes all task decisions autonomously. The sources describe different systems and workflows, not one standard architecture.
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Start with the job a system performs. The table separates the broad workflow types; it is not a ranking, and the evidence available does not establish comparable cost, uptime, safety, or throughput across them.
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| Approach | What moves | Who selects the individual item? | What to examine |
|---|---|---|---|
| Goods-to-person / pod-to-picker | A mobile drive brings an inventory-bearing pod to an associate. | The associate in the described workflow. | Pod-selection logic and drive distance per pick; the Amazon study reports results for a particular redesigned algorithm and implementation. |
| Collaborative AMR picking | A mobile robot carries goods, carts, or orders to support human work. | People may pick in the collaborative workflow described by DHL. | How robot tasks fit into live fulfillment and human work; deployment claims should remain attributed to the reporting company. |
| Robotic item picking | A robotic arm moves to and grasps an individual item. | The robot attempts the grasp. | Perception, motion planning, and execution for the particular task; do not assume every arm handles every item. |
| Automated storage and retrieval | Storage and inventory are handled through an automated storage/retrieval category. | Not stated in the reviewed overview. | The category may coexist with other automation, but the reviewed material does not provide a detailed system specification. |
Before treating two deployments as comparable, check what each one automates: pods, totes, carts, cases, or individual items; whether inventory comes to a worker or the robot goes to the inventory; whether the person or robot performs the pick; and how task assignment connects to warehouse and fleet software. Then inspect how the reported result was measured and who reported it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the reported performance figures do—and do not—show
Published figures can illustrate the effect of a particular design or deployment, but they are not universal forecasts. The following results have different sources and scopes:
| Reported figure | Source and scope | How to interpret it |
|---|---|---|
| 62% reduction in drive distance per unit picked; 31% reduction in the number of drives required; half a billion dollars in savings | Authors of a 2023 article in INFORMS Journal on Applied Analytics, reporting Amazon Robotics’ redesigned pod-selection algorithm and its implementation. | These are results reported for that algorithm and implementation, not a guarantee for another warehouse or robot fleet. |
| 500 million picks | DHL Group said the milestone pick occurred on May 18, 2024, at its Toledo, Spain facility using Locus Robotics AMRs. | A company-reported cumulative milestone at one named facility; it is not a comparative productivity or accuracy benchmark. |
| More than 35 DHL locations | DHL Delivered’s 2024 coverage of LocusBot operations. | A time-bound, company-reported deployment count, not a current fleet total or independent measure of results. |
| Over 15% higher picking efficiency and 50% less employee travel time | Zebra Technologies’ undated JAS Worldwide customer case study. | Attribute both outcomes to that case study; they are not independently verified or established as typical results. |
| 70% of items shipped through the robotics system | Amazon robotics director Julie Mitchell, quoted by the Associated Press in 2024 about one Nashville-area building during the deployment period described in the interview. | A facility-specific, Amazon-reported figure—not a rate for Amazon warehouses generally or for warehouse robots as a category. |
The figures measure different things: travel or drive distance, number of drives, claimed savings, picks completed, locations using a system, efficiency, employee travel time, and the share of items passing through a specific building’s robotics system. They should not be combined into a single ranking. The available sources do not provide independent, comparable cross-vendor benchmarks or a comprehensive cost comparison.
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A warehouse’s requirements depend on its workflow, but these checks help prevent a transport robot from being mistaken for a universal picker or a vendor-specific result from being treated as a general promise:
- What is the robot actually handling? Identify whether it transports pods, totes, carts, or orders—or grasps individual items.
- Who performs the pick? Determine whether the person selects the unit or an arm attempts to grasp it.
- What decisions does software support? Ask how tasks are assigned and coordinated with orders and human work, rather than assuming the robot makes every decision by itself.
- What does the performance number measure? Separate a deployment count, a facility-specific share, a movement reduction, and an efficiency claim; they are not equivalent outcomes.
- Who reported the result, and where? Keep company, vendor, case-study, and academic reports clearly attributed and scoped to the named system or implementation.
- What evidence is missing? The cited material does not establish independent comparable figures for total cost, uptime, safety, or throughput across these system types. Avoid choosing a “best” robot on the basis of the figures above alone.
The practical distinction
Warehouse robots can move inventory, bring it to a person, support a picker, or attempt to grasp items themselves. AI and control software help coordinate those tasks, but the capabilities differ by workflow and system. To understand what a deployment really automates, identify what moves, who picks the item, and what the reported performance evidence actually measures.
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