Tag Archives: logistics
Open AI’s Dota 2 AI Steamrolls World Champion e-Sports Team With Back-to-Back Victories
Nick Statt | The Verge
“…[OpenAI cofounder and CEO, Sam Altman] tells me there probably does not exist a video game out there right now that a system like OpenAI Five can’t eventually master at a level beyond human capability. For the broader AI industry, mastering video games may soon become passé, simple table stakes required to prove your system can learn fast and act in a way required to tackle tougher, real-world tasks with more meaningful benefits.”
Boston Dynamics Debuts the Production Version of SpotMini
Brian Heater, Catherine Shu | TechCrunch
“SpotMini is the first commercial robot Boston Dynamics is set to release, but as we learned earlier, it certainly won’t be the last. The company is looking to its wheeled Handle robot in an effort to push into the logistics space. It’s a super-hot category for robotics right now. Notably, Amazon recently acquired Colorado-based start up Canvas to add to its own arm of fulfillment center robots.”
Scientists Restore Some Brain Cell Functions in Pigs Four Hours After Death
Joel Achenbach | The Washington Post
“The ethicists say this research can blur the line between life and death, and could complicate the protocols for organ donation, which rely on a clear determination of when a person is dead and beyond resuscitation.”
How Scientists 3D Printed a Tiny Heart From Human Cells
Yasmin Saplakoglu | Live Science
“Though the heart is much smaller than a human’s (it’s only the size of a rabbit’s), and there’s still a long way to go until it functions like a normal heart, the proof-of-concept experiment could eventually lead to personalized organs or tissues that could be used in the human body…”
The Next Clash of Silicon Valley Titans Will Take Place in Space
Luke Dormehl | Digital Trends
“With bold plans that call for thousands of new satellites being put into orbit and astronomical costs, it’s going to be fascinating to observe the next phase of the tech platform battle being fought not on our desktops or mobile devices in our pockets, but outside of Earth’s atmosphere.”
The Images That Could Help Rebuild Notre-Dame Cathedral
Alexis C. Madrigal | The Atlantic
“…in 2010, [Andrew] Tallon, an art professor at Vassar, took a Leica ScanStation C10 to Notre-Dame and, with the assistance of Columbia’s Paul Blaer, began to painstakingly scan every piece of the structure, inside and out. …Over five days, they positioned the scanner again and again—50 times in all—to create an unmatched record of the reality of one of the world’s most awe-inspiring buildings, represented as a series of points in space.”
Mapping Our World in 3D Will Let Us Paint Streets With Augmented Reality
Charlotte Jee | MIT Technology Review
“Scape wants to use its location services to become the underlying infrastructure upon which driverless cars, robotics, and augmented-reality services sit. ‘Our end goal is a one-to-one map of the world covering everything,’ says Miller. ‘Our ambition is to be as invisible as GPS is today.’i”
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Robots have been masters of manufacturing at speed and precision for decades, but give them a seemingly simple task like stacking shelves, and they quickly get stuck. That’s changing, though, as engineers build systems that can take on the deceptively tricky tasks most humans can do with their eyes closed.
Boston Dynamics is famous for dramatic reveals of robots performing mind-blowing feats that also leave you scratching your head as to what the market is—think the bipedal Atlas doing backflips or Spot the galloping robot dog.
Last week, the company released a video of a robot called Handle that looks like an ostrich on wheels carrying out the seemingly mundane task of stacking boxes in a warehouse.
It might seem like a step backward, but this is exactly the kind of practical task robots have long struggled with. While the speed and precision of industrial robots has seen them take over many functions in modern factories, they’re generally limited to highly prescribed tasks carried out in meticulously-controlled environments.
That’s because despite their mechanical sophistication, most are still surprisingly dumb. They can carry out precision welding on a car or rapidly assemble electronics, but only by rigidly following a prescribed set of motions. Moving cardboard boxes around a warehouse might seem simple to a human, but it actually involves a variety of tasks machines still find pretty difficult—perceiving your surroundings, navigating, and interacting with objects in a dynamic environment.
But the release of this video suggests Boston Dynamics thinks these kinds of applications are close to prime time. Last week the company doubled down by announcing the acquisition of start-up Kinema Systems, which builds computer vision systems for robots working in warehouses.
It’s not the only company making strides in this area. On the same day the video went live, Google unveiled a robot arm called TossingBot that can pick random objects from a box and quickly toss them into another container beyond its reach, which could prove very useful for sorting items in a warehouse. The machine can train on new objects in just an hour or two, and can pick and toss up to 500 items an hour with better accuracy than any of the humans who tried the task.
And an apple-picking robot built by Abundant Robotics is currently on New Zealand farms navigating between rows of apple trees using LIDAR and computer vision to single out ripe apples before using a vacuum tube to suck them off the tree.
In most cases, advances in machine learning and computer vision brought about by the recent AI boom are the keys to these rapidly improving capabilities. Robots have historically had to be painstakingly programmed by humans to solve each new task, but deep learning is making it possible for them to quickly train themselves on a variety of perception, navigation, and dexterity tasks.
It’s not been simple, though, and the application of deep learning in robotics has lagged behind other areas. A major limitation is that the process typically requires huge amounts of training data. That’s fine when you’re dealing with image classification, but when that data needs to be generated by real-world robots it can make the approach impractical. Simulations offer the possibility to run this training faster than real time, but it’s proved difficult to translate policies learned in virtual environments into the real world.
Recent years have seen significant progress on these fronts, though, and the increasing integration of modern machine learning with robotics. In October, OpenAI imbued a robotic hand with human-level dexterity by training an algorithm in a simulation using reinforcement learning before transferring it to the real-world device. The key to ensuring the translation went smoothly was injecting random noise into the simulation to mimic some of the unpredictability of the real world.
And just a couple of weeks ago, MIT researchers demonstrated a new technique that let a robot arm learn to manipulate new objects with far less training data than is usually required. By getting the algorithm to focus on a few key points on the object necessary for picking it up, the system could learn to pick up a previously unseen object after seeing only a few dozen examples (rather than the hundreds or thousands typically required).
How quickly these innovations will trickle down to practical applications remains to be seen, but a number of startups as well as logistics behemoth Amazon are developing robots designed to flexibly pick and place the wide variety of items found in your average warehouse.
Whether the economics of using robots to replace humans at these kinds of menial tasks makes sense yet is still unclear. The collapse of collaborative robotics pioneer Rethink Robotics last year suggests there are still plenty of challenges.
But at the same time, the number of robotic warehouses is expected to leap from 4,000 today to 50,000 by 2025. It may not be long until robots are muscling in on tasks we’ve long assumed only humans could do.
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Image Source The 1SHIFT Logistics platform, developed by LiteLink Technologies, shows what can happen when the technology industry meets the logistics industry. 1SHIFT Logistics uses advanced technology such as artificial intelligence and geolocation to coordinate all three major parts of the logistics process: the shipper, the carrier and the delivery site. When most people think …
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