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What “stowing” means in an Amazon fulfillment center
Inbound products arrive at a fulfillment center and are moved into totes. A worker or automated system places each item into a coded bin in a large yellow storage pod. Mobile robots later bring those pods to picking stations when customer orders require the contents. Amazon’s fulfillment-center tour describes this basic flow at Amazon’s fulfillment-center photo tour.
Stowing is not simply putting an item on an empty shelf. The pod is soft-sided, its opening is crossed by elastic strips, and the bin may already contain a random assortment of clothing, toys, books, sports equipment, and small packaged goods. The incoming product must fit, remain undamaged, and stay accessible for a later pick.
Why stowing is harder than picking
Picking and stowing share cameras, manipulators, and clutter, but they pose different decisions. Picking usually starts with a target item that must be identified and removed. Stowing starts with a known incoming item and asks where it can fit, what existing items can move, and how to create a contiguous pocket of space.
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Products vary in size, mass, rigidity, deformability, packaging, friction, and center of gravity. Elastic bands partly hide the bin, so the robot cannot simply measure an unobstructed cavity. A pod can also contain a different number or arrangement of items than the inventory record indicates, a discrepancy IEEE Spectrum describes in its feature on the project: IEEE Spectrum’s report on Amazon’s warehouse robots.
That makes the task a physical reasoning problem. Apparent empty area may be too narrow for the product; usable space may become available only after moving another item; and an apparently safe push can cause a neighboring object to roll, deform, or become inaccessible.
Why conventional industrial robots struggled
Traditional industrial robots excel when objects, fixtures, and trajectories are repeatable. They are commonly programmed to reach known coordinates while avoiding unexpected contact. A stowing robot must instead make contact deliberately and react to whatever resistance it encounters.
Position control versus compliant manipulation
Position control commands the arm to follow a predefined path. Compliant manipulation adjusts that path using contact forces. Amazon says the arm selected for the project supplied force feedback hundreds of times per second, allowing the system to detect resistance and change its behavior. The distinction matters when a product catches on an elastic band, meets another item, or encounters a bin wall earlier than expected.
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The hardware that makes the work possible
Hook for the elastic band
A hook lifts or moves the elastic strip across the front of a pod bin. The mechanism is simple, but essential: a robot designed for rigid, open shelving would not have direct access to this storage environment.
Paddle gripper
Two paddles gently squeeze and hold the incoming product. Amazon’s researchers found this more suitable for the relevant product mix than relying only on suction cups or conventional pinchers, which can struggle with porous, soft, or irregular packaging.
Miniature conveyor belts
Early paddle designs interfered with insertion. A drop-and-push method improved access but produced inconsistent results because products responded differently to the push. The later design put miniature conveyor belts on the paddles. The belts feed the item into the bin while the paddles remain nearer the opening, separating secure holding from deep insertion. Amazon reported that this change raised success from roughly 80% to 99% at that development stage; it was not a universal production rate.
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A thin, extendable metal sheet, described as a spatula, rearranges items already in the bin. It can sweep objects sideways, flip a flat item, consolidate or stack contents, and slide an item diagonally into a gap. Keeping this tool separate from the paddles prevents the grasping assembly from blocking the space-making motions.
Seeing space that is not obvious
The cameras must estimate more than visible floor area. The system reasons about whether a bin has capacity, whether empty pockets can be consolidated, which objects can be moved safely, and which sequence is likely to make room for the incoming product. The elastic bands create occlusion, so perception models or infers portions of the arrangement that cannot be seen directly.
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- Available space: area that appears empty from the current view.
- Contiguous usable space: a connected region large and shaped appropriately for the incoming item.
- Creatable space: capacity that can be produced by safely moving existing contents.
This distinction explains why a nearly full bin can still accept an item, while a bin with visible gaps may not.
Motion primitives give the robot a reusable vocabulary
Amazon researchers studied how people stow and reduced common space-making actions to reusable motion types:
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- Sideways sweeping.
- Flipping an item upright.
- Stacking or consolidating items.
- Slotting an item diagonally between neighboring objects.
Machine-learning models select and sequence these primitives instead of learning an entirely new trajectory for every product and bin. This middle ground is important: fixed paths are brittle in clutter, while unconstrained end-to-end behavior is difficult to validate. The primitives provide structure; force feedback lets execution adapt to contact.
How the buffer and confidence decision work
Products waiting for the workcell sit in a staging buffer, while the pod offers multiple candidate bins. The planner evaluates possible item-bin pairings and predicts the likely success of different motion sequences. It can choose a different item when another product fits more naturally, rather than treating the queue as a fixed order.
Amazon reported that when the best predicted success probability fell to about 96%, typically as a pod approached capacity, the system sent that pod away and brought in another. This is a throughput decision threshold, not a claim that the model is 96% accurate overall. A practical production system can preserve flow by declining a low-confidence attempt and routing the remainder to people.
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What the reported numbers actually mean
| Figure | Meaning | Qualification |
|---|---|---|
| About 80% to 99% | Improvement after adding miniature conveyor belts to the end-of-arm tool | Amazon-reported development-stage result, not a fleet-wide rate |
| 94 of 95 items | Result in a test containing selected challenging product attributes | Specific sample; it does not mean 98.9% of all Amazon products are robot-stowable |
| About 96% | Predicted-success threshold for sending away a nearly full pod | Decision threshold, not measured overall accuracy |
| 85% | Ultimate target for products stocked by a standard fulfillment center | Target, not a verified network-wide result |
IEEE Spectrum separately described laboratory success above 90% and repeated the 85% coverage goal. Those metrics describe different things: success is the outcome on attempted cases, while coverage is the share of inventory the system is intended to handle.
Products and situations that remain difficult
Amazon identified particularly bulky items, particularly heavy items, and cylindrical products that do not behave reliably on the conveyor mechanism as possible exclusions. Other engineering edge cases follow from the physical setup, although Amazon did not publish fleet-wide failure rates for them:
- Soft bags that deform unpredictably.
- Products with offset centers of gravity.
- Slippery or reflective packaging.
- Items that roll after insertion.
- Dimensions that differ from catalog data.
- A bin whose actual contents differ from its database record.
- An object that becomes trapped after a rearrangement.
A robust system therefore needs detection and recovery, not just a successful grasp. If the confidence estimate is low or the physical response differs from the model, the safe action can be to stop and hand the case to a worker.
Why Amazon uses a human-robot workflow
The project was presented as hybrid automation. Robots perform repetitive manipulation and reach awkward pod positions; people handle unusual, bulky, heavy, or otherwise difficult products. The design is intended to fit an existing “brownfield” building and workflow rather than require every fulfillment center to be rebuilt.
Amazon has described ergonomic and safety benefits, but those are company claims rather than independent measurements of injury rates or worker experience. The technical contribution is narrower and more concrete: automate a valuable subset while preserving a human path for exceptions.
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Later announcements show broader investment, but they should not be confused with the fabric-pod stowing prototype.
- In October 2023, Amazon announced Sequoia, a containerized-storage system combining mobile robots, gantries, robotic arms, and ergonomic workstations. Amazon said it was operating at a Houston fulfillment center and could identify and store inbound inventory up to 75% faster while reducing order-processing time by up to 25%: Amazon’s Sequoia announcement.
- In October 2024, Amazon described a next-generation Shreveport, Louisiana, facility using Sequoia, Sparrow, Cardinal, and Robin: Amazon’s Shreveport robotics overview.
- In June 2025, Amazon said it had deployed its one-millionth robot across more than 300 facilities and introduced DeepFleet for coordinating robot movement. That milestone does not establish deployment of this specific stowing machine: Amazon’s fleet and DeepFleet announcement.
- Amazon introduced Blue Jay in October 2025 as a multi-arm system for picking, stowing, and consolidation, with a reported test covering about 75% of item types. An update dated February 25, 2026, said Blue Jay was no longer being used in operations: Amazon’s Blue Jay announcement and update.
- As of June 2026, Amazon was still announcing tactile and collaborative systems, including Vulcan and next-generation Proteus robots. These developments show continued investment, not the production status of the original stower: Amazon’s Vulcan and Proteus update.
What “solving” the beautiful problem really means
The breakthrough is not a universal robot that can pack every product into every bin. It is an architecture that combines visual inference, compliant contact, specialized tooling, reusable motions, confidence-based decisions, and human exception handling. That combination can make partial automation worthwhile in an environment where perfect inventory records, open shelves, and identical products cannot be assumed.
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