Connect with us

Tech

AI system learns to keep warehouse robot traffic running smoothly

Published

on

AI system learns to keep warehouse robot traffic running smoothly



Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a steady stream of customer orders. In this busy environment, even small traffic jams or minor collisions can snowball into massive slowdowns.

To avoid such an avalanche of inefficiencies, researchers from MIT and the tech firm Symbotic developed a new method that automatically keeps a fleet of robots moving smoothly. Their method learns which robots should go first at each moment, based on how congestion is forming, and adapts to prioritize robots that are about to get stuck. In this way, the system can reroute robots in advance to avoid bottlenecks.

The hybrid system utilizes deep reinforcement learning, a powerful artificial intelligence method for solving complex problems, to figure out which robots should be prioritized. Then, a fast and reliable planning algorithm feeds instructions to the robots, enabling them to respond rapidly in constantly changing conditions.

In simulations inspired by actual e-commerce warehouse layouts, this new approach achieved about a 25 percent gain in throughput over other methods. Importantly, the system can quickly adapt to new environments with different quantities of robots or varied warehouse layouts.

“There are a lot of decision-making problems in manufacturing and logistics where companies rely on algorithms designed by human experts. But we have shown that, with the power of deep reinforcement learning, we can achieve super-human performance. This is a very promising approach, because in these giant warehouses even a 2 or 3 percent increase in throughput can have a huge impact,” says Han Zheng, a graduate student in the Laboratory for Information and Decision Systems (LIDS) at MIT and lead author of a paper on this new approach.

Zheng is joined on the paper by Yining Ma, a LIDS postdoc; Brandon Araki and Jingkai Chen of Symbotic; and senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of LIDS. The research appears today in the Journal of Artificial Intelligence Research.

Rerouting robots

Coordinating hundreds of robots in an e-commerce warehouse simultaneously is no easy task.

The problem is especially complicated because the warehouse is a dynamic environment, and robots continually receive new tasks after reaching their goals. They need to be rapidly redirected as they leave and enter the warehouse floor.

Companies often leverage algorithms written by human experts to determine where and when robots should move to maximize the number of packages they can handle.

But if there is congestion or a collision, a firm may have no choice but to shut down the entire warehouse for hours to manually sort the problem out.

“In this setting, we don’t have an exact prediction of the future. We only know what the future might hold, in terms of the packages that come in or the distribution of future orders. The planning system needs to be adaptive to these changes as the warehouse operations go on,” Zheng says.

The MIT researchers achieved this adaptability using machine learning. They began by designing a neural network model to take observations of the warehouse environment and decide how to prioritize the robots. They train this model using deep reinforcement learning, a trial-and-error method in which the model learns to control robots in simulations that mimic actual warehouses. The model is rewarded for making decisions that increase overall throughput while avoiding conflicts.

Over time, the neural network learns to coordinate many robots efficiently.

“By interacting with simulations inspired by real warehouse layouts, our system receives feedback that we use to make its decision-making more intelligent. The trained neural network can then adapt to warehouses with different layouts,” Zheng explains.

It is designed to capture the long-term constraints and obstacles in each robot’s path, while also considering dynamic interactions between robots as they move through the warehouse.

By predicting current and future robot interactions, the model plans to avoid congestion before it happens.

After the neural network decides which robots should receive priority, the system employs a tried-and-true planning algorithm to tell each robot how to move from one point to another. This efficient algorithm helps the robots react quickly in the changing warehouse environment.

This combination of methods is key.

“This hybrid approach builds on my group’s work on how to achieve the best of both worlds between machine learning and classical optimization methods. Pure machine-learning methods still struggle to solve complex optimization problems, and yet it is extremely time- and labor-intensive for human experts to design effective methods. But together, using expert-designed methods the right way can tremendously simplify the machine learning task,” says Wu.

Overcoming complexity

Once the researchers trained the neural network, they tested the system in simulated warehouses that were different than those it had seen during training. Since industrial simulations were too inefficient for this complex problem, the researchers designed their own environments to mimic what happens in actual warehouses.

On average, their hybrid learning-based approach achieved 25 percent greater throughput than traditional algorithms as well as a random search method, in terms of number of packages delivered per robot. Their approach could also generate feasible robot path plans that overcame congestion caused by traditional methods.

“Especially when the density of robots in the warehouse goes up, the complexity scales exponentially, and these traditional methods quickly start to break down. In these environments, our method is much more efficient,” Zheng says.

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 in warehouse automation.

In the future, the researchers want to include task assignments in the problem formulation, since determining which robot will complete each task impacts congestion. They also plan to scale up their system to larger warehouses with thousands of robots.

This research was funded by Symbotic.



Source link

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Tech

Could Contact-Tracing Apps Help With the Hantavirus? Not Really

Published

on

Could Contact-Tracing Apps Help With the Hantavirus? Not Really


After three people died on a cruise ship struck by a hantavirus, authorities are actively tracking down 29 people who had left the ship. They’re trying to trace the spread of the virus. It’s a long, arduous, global process to find and notify people who might be at risk of infection.

Hey, wasn’t there supposed to be an app for that?

Contact-tracing apps were a global effort starting in 2020 during the Covid-19 pandemic. Enabled by phone companies like Apple and Google, contact tracing was designed to use Bluetooth connections to detect when people had come in contact with someone who had or would later test positive for Covid and report as much. It didn’t do much to solve the spread of the pandemic, but tracking the virus became more effective at least. The same process wouldn’t go well for the hantavirus problem.

“There is no use of apps for this hantavirus outbreak,” Emily Gurley, an epidemiologist at Johns Hopkins University, wrote in an email response to WIRED. “The number of cases are small, and it’s important to trace all contacts exactly to stop transmission.”

On a smaller scale of infection like this, officials have to start at the source (an infected individual), then go person-by-person, confirming where they went and who they might have come into contact with. Data collected by apps from a broad swath of devices would not be anywhere close to accurate enough to give a good idea of where the virus might have hitchhiked to next.

Contact tracing on a wider scale, like, say, a global pandemic, is less about tracking the individual infections and more about understanding what parts of the population might be affected, giving people the opportunity to self-quarantine after exposure. But that depends on how people choose to respond, and how the technology is utilized by public emergency systems. During the Covid pandemic, contact-tracing via apps tended to work better in more carefully managed European countries, but did not slow the spread in the US.

Making devices accessible to that kind of proximity information has also brought all sorts of concerns about privacy, given that the technology would require always-on access to work properly. Contact tracing also struggled to maintain accuracy, and in some cases could be providing false negatives or positives that don’t help further real information about the spread of the virus.

Especially in the case of something like the Hantavirus, where every person on that cruise ship can theoretically be directly tracked and contacted, it’s better to do that process the hard way.

“During small but highly fatal outbreaks, more precision is required,” Gurley wrote.



Source link

Continue Reading

Tech

‘Reservation Hijacking’ Scams Target Travelers. Here’s How to Stay Safe

Published

on

‘Reservation Hijacking’ Scams Target Travelers. Here’s How to Stay Safe


There’s another type of digital scam to be aware of, as per the BBC. It’s called “reservation hijacking.”

The name gives you a clue as to how it works. Essentially, scammers use details about a booking you’ve placed (perhaps with a hotel or airline) to trick you into sending money somewhere you shouldn’t.

While this type of scam isn’t brand new, a recent data breach at Booking.com has raised the risk of people being caught out. With data about you and your reservation, a far more convincing setup can be put in place—why wouldn’t you believe that someone purporting to be an employee from a spa you’ve got a reservation with is telling the truth about who they are, especially if they know the dates of your trip, your phone number, and your email address?

According to Booking.com, no financial information was exposed in the April 2026 hack. However, names, email addresses, phone numbers, and booking details have been leaked. The travel portal says affected customers have been emailed about the heightened risk of scams, so that’s the first thing to check for when it comes to staying safe.

Minimizing the risk of getting scammed by a reservation hijack involves many of the same security precautions you may already be following, and just being aware that this is a way you might be targeted will make a difference.

How Reservation Hijacks Work

Scammers can get hold of your booking details.

Courtesy of David Nield

We’ve already outlined the basics of a reservation hijack, but it can take several forms. As with other types of scams, it tends to evolve over time. The basic premise is that someone will get in touch with you claiming to be from a place you have a reservation with, whether it’s a car rental company or a hotel.

The scammers will try to pull together as much information as they can on you and your booking. Sometimes they’ll target employees of the place you’ve got the reservation with in order to get access to their systems, and other times they may take advantage of a wider data breach (as with the recent Booking.com hack).

They might also get information through other means. Maybe they’ve somehow got access to your email, or to some of your social media posts (where you’ve shared your next vacation destination and a countdown of how many days are left to go). Don’t be caught out if you find yourself speaking to someone who knows a lot about your travel plans.



Source link

Continue Reading

Tech

I Tried the Best Captioning Smart Glasses, and Only One Leads the Pack

Published

on

I Tried the Best Captioning Smart Glasses, and Only One Leads the Pack


Unlike the other glasses I tested, Even doesn’t sell a subscription plan; everything’s included out of the box.

The only downside I could find with the G2 is that it is largely devoid of offline features, so the glasses have to be connected to the internet to do much of anything. Considering the G2’s capabilities, it’s a trade-off I am more than happy to make.

Other Captioning Glasses I Tested

There are plenty of capable captioning eyeglasses on the market, but they are surprisingly similar in both looks and features. While many are quite capable, none had the combination of power and affordability that I got with Even’s G2. Here’s a rundown of everything else I tested.

  • Photograph: Christopher Null

  • Photograph: Christopher Null

  • Photograph: Christopher Null

Leion’s Hey 2 is the price leader in this market, and even its prescription lenses ($90 to $299) are pretty affordable. The hardware, however, is heavy: 50 grams without lenses, 60 grams with them. A full charge gets you six to eight hours of operation; the case adds juice for up to 12 recharges.

I like the Leion interface, which lays out caption, translation, “free talk” (two-way translation), and a teleprompter feature on its clean app. You get access to nine languages; using Pro minutes expands that to 143. Leion sells its premium plan by the minute, not the month, so you need to remember to toggle this mode off when you don’t need it. Pricing is $10 for 120 minutes, $50 for 1,200 minutes, and $200 for 6,000 minutes. There’s no offline use supported, and I often struggled to get AI summaries to show up in English instead of Chinese (regardless of the recorded language).

  • Photograph: Christopher Null

  • Photograph: Christopher Null

You’re not seeing double: XRAI and Leion use the same manufacturer for their hardware, and the glasses weigh the same. The battery spec is also similar, with up to eight hours on the frames and another 96 hours when recharging with the case. XRAI claims its display is significantly brighter than competitors’, but I didn’t see much of a difference in day-to-day use.

The features and user experience are roughly the same, though Leion’s teleprompter feature isn’t implemented in XRAI’s app, and it doesn’t offer AI summaries of conversations. I also didn’t find XRAI’s app as user-friendly as Leion’s version, particularly when trying to switch among the admittedly exhaustive 300 language options. Only 20 of these are included without ponying up for a Pro subscription, which is sold both by the month and minute: $20/month gets you a max of 600 upgraded transcription minutes and 300 translation minutes; $40/month gets you 1,800 and 1,200 minutes, respectively. On the plus side, XRAI does have a rudimentary offline mode that works better than most. For prescription lenses, add $140 to $170.

  • Photograph: Christopher Null

  • Photograph: Christopher Null

AirCaps

AirCaps Smart Glasses

AirCaps does not make its own prescription lenses. Instead, you must purchase a pair of $39 “lens holders” and take them to an optician if you want prescription inserts. I was unable to test these with prescription lenses and ultimately had to try them out over my regular glasses, which worked well enough for short-term testing. Frames weigh a hefty 53 grams without add-on lenses; the company couldn’t tell me how much extra weight prescription lenses would add to that, but it’s safe to say these are the bulkiest and heaviest captioning glasses on the market. Despite the weight, they only carry two to four hours of battery life, with 10 or so recharges packed into the comically large case. Another option is to clip one of AirCaps’ rechargeable 13-gram Power Capsules ($79 for two) to one of the arms, which can provide 12 to 18 extra hours of juice.

The AirCaps feature list and interface make it perhaps the simplest of all these devices, with just a single button to start and stop recording. Transcriptions and translations are available for free in nine languages. For $20/month, you can add the Pro package, which offers better accuracy, access to more than 60 languages, and the option to generate AI summaries on demand (though only if recordings are long enough). As a bonus: Five hours of Pro features are free each month. Offline mode works pretty well, too. The only bad news is that these bulky frames just aren’t comfortable enough for long-term wear.

  • Photograph: Christopher Null

  • Photograph: Christopher Null

The most expensive option on the market (up to $1,399 with prescription lenses!) weighs a relatively svelte 40 grams (52 grams with lenses) and offers about four hours of battery life. There’s no charging case; the glasses must be charged directly using the included USB-connected dongle.

The glasses are extremely simple, offering transcription and translation features—with support for about 80 languages, which is impressive. I unfortunately found the prescription lenses Captify sent to be the blurriest of the bunch, making the captions comparatively hard to read. And while the device supports offline transcription, performance suffered badly when disconnected from the internet. I couldn’t get translations to work at all when offline. For $15/month, you get better accuracy and speaker differentiation, and access to AI summaries of conversations. Prescription lenses cost between $99 and $600.



Source link

Continue Reading

Trending