AI system learns to prevent warehouse robot traffic jams, boosting throughput 25%

A warehouse robot traffic jam sounds like a minor problem. It is not. In a facility running thousands of autonomous units, a single bot caught in a deadlock can cascade into hours of lost throughput. The fix, according to a paper published March 24, 2026 in the Journal of Artificial Intelligence Research, involves a reinforcement learning algorithm that predicts where robots will get stuck hours before they actually do, rerouting them preemptively. In simulations of real warehouse layouts, the system delivered roughly a 25 percent throughput gain over existing routing methods, according to MIT News.

The research was funded by Symbotic, the warehouse robotics company that deploys its systems across all 42 of Walmart's U.S. regional distribution centers, reaching 1,400 stores. The lead author is Han Zheng, a graduate student at MIT's Laboratory for Information and Decision Systems. The senior author is Cathy Wu, an associate professor in MIT's Department of Civil and Environmental Engineering. The paper describes a genuine advance in multi-agent coordination — the kind of fundamental logistics problem that, if it translates to the real world, changes how mega-warehouses operate at scale.

But the paper itself is careful about what it is claiming. "While their system is still far away from real-world deployment, these demonstrations highlight the feasibility and benefits of using a machine learning-guided approach," the authors note. The gains are real in simulation. They have not been demonstrated in an operating facility with real inventory and real workers moving around real robots.

Zheng put it plainly in the MIT interview: "In these giant warehouses even a 2 or 3 percent increase in throughput can have a huge impact."

That sentence is doing a lot of work. The 25 percent figure is the headline. The 2 to 3 percent extrapolation is the subtext. And the subtext is where the workforce math lives.

Symbotic reported $2.247 billion in revenue for fiscal year 2025, up approximately 26 percent year-over-year. In May 2025, it cut 400 jobs at its Andover, Massachusetts location, according to WARN notice filings with the state. The cuts took effect June 27. Those layoffs came five months after Symbotic acquired Walmart's Advanced Systems and Robotics business — the unit it now uses to run robots inside Walmart's own distribution network — in a deal valued at $200 million upfront plus up to $350 million in additional payments. The company had previously cut 200 jobs in 2023 when it outsourced some manufacturing. Symbotic trades on Nasdaq under the ticker SYM.

So here is the timeline: Symbotic publishes research showing its systems can run warehouses more efficiently. It simultaneously cuts hundreds of jobs. It reports record revenue. It acquires the robotics unit of the largest retailer in the United States. None of this is coincidental. The efficiency gains being documented in academic papers and press releases are the same math that makes a workforce reduction possible.

Amazon, the largest private employer in the U.S. warehouse sector, has outlined plans to reduce its warehouse headcount by 600,000 workers through automation by 2033 — workers it hopes not to hire, rather than existing staff slated for replacement — generating estimated savings of $2 billion to $4 billion annually starting in 2027, according to New York Times reporting on Amazon internal strategy documents. Senator Bernie Sanders cited those projections in a letter to Amazon CEO Andy Jassy. In March 2026, Amazon's robotics unit cut at least 100 white-collar jobs, Reuters reported. The trajectory is documented. The targets are public.

The MIT-Symbotic paper is peer-reviewed and published in JAIR. The 25 percent throughput gain is a real result in a well-designed simulation. Han Zheng and Cathy Wu did careful work. None of that changes what the efficiency number means at scale: fewer workers doing the same volume, or the same workers doing substantially more.

Walmart's own CFO has described the goal of its automation investments as alleviating labor-intensive warehouse jobs, such as unloading trucks. The company invested $150 million in Symbotic via a PIPE at the 2022 SPAC merger that took Symbotic public. Its 42 regional distribution centers now run Symbotic's autonomous bots, which travel at 25 miles per hour. The software coordinates them across racks, induction stations, and delivery zones. The system works.

What the system does not do, automatically, is share the gains with the people whose jobs the gains make expendable. Symbotic's GreenBox joint venture with SoftBank — 65 percent SoftBank, 35 percent Symbotic — holds approximately $7.5 billion in system contracts over the life of those agreements. The robotics business is healthy. The company is hiring people who can build the robots. It is laying off the people who worked next to them.

The paper is titled with a logistics problem. The real subject is the timing. Symbotic published its efficiency research while cutting 400 jobs. Amazon published its automation targets while cutting robotics staff. The workers on the warehouse floor are not named in the academic literature. They do show up in the WARN notices. They show up in the headcount. They show up in the math.

Han Zheng is right that a 2 or 3 percent efficiency gain matters in a giant warehouse. It also matters in a workforce planning model. Those are not separate questions. The MIT-Symbotic paper and the 400 layoffs in Andover are the same story, told from different floors of the same building.