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Main article: Robotics

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Rocket Lab’s new ‘Rosie the Robot’ speeds up launch vehicle production – by a lot

21:17 | 13 November

Rocket launch startup Rocket Lab is all about building out rapid-response space launch capabilities, and founder/CEO Peter Beck is showing off its latest advancement in service of that goal: A room-sized manufacturing robot named ‘Rosie.’

Rosie is tasked with processing the carbon composite components of Rocket Lab’s Electron launch vehicle. That translates to basically getting the rocket flight ready, and there’s a lot involved in that – it’s a process that normally can take “hundreds of hours,” according to Beck. So how fast can Rosie manage the same task?

“We can produce one launch vehicle in this machine every 12 hours,” Beck says in the video. That includes “every bit of marking, every bit of machining, every bit of drilling,” he adds.

This key new automation tool essentially turns something that was highly bespoke and manual, and turns it into something eminently repeatable and expedited, which is a necessary ingredient if Rocket Lab is ever to accomplish its goal of providing high-frequency launches to small satellite customers with very little turnaround time. The company’s New Zealand launch facility recently landed an FAA license that helps sketch out the extent of its ambition, since it’s technically cleared to launch rockets as often as every 72 hours.

In addition to innovations like Rosie, Rocket Lab uses 3D printing for components of its launch vehicle engines that result in single-day turnaround for production, vs. weeks using more traditional methods. It’s also now working on an ambitious plan for rocket recovery, which should help further with providing high-frequency launch capabilities since it’ll mean you don’t have to build entirely new launch vehicles for every mission.

 


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Adidas backpedals on robotic shoe production with Speedfactory closures

01:38 | 12 November

An expensive experiment in global distribution has been abandoned by Adidas, which has announced that will close its robotic “Speedfactories” in Atlanta and Ansbach, Germany, within 6 months. The company sugar-coated the news with a promise to repurpose the technology used at its existing human-powered factories in Asia.

The factories were established in 2016 (Ansbach) and 2017 (Atlanta) as part of a strategy to decentralize its manufacturing processes. The existing model, like so many other industries, is to produce the product in eastern Asia, where labor and overhead is less expensive, then ship it as needed. But this is a slow and clumsy model for an industry that moves as quickly as fashion and athletics.

“Right now, most of our products are made out of Asia and we put them on a boat or on a plane so they end up on Fifth Avenue,” said Adidas CMO Eric Liedtke in an interview last year at Disrupt SF about new manufacturing techniques. The Speedfactories were intended to change that: “Instead of having some sort of micro-distribution center in Jersey, we can have a micro-factory in Jersey.”

Ultimately this seems to have proven more difficult than expected. As other industries have found in the rush to automation, it’s easy to overshoot the mark and overcommit when the technology just isn’t ready.

Robotic factories are a powerful tool but difficult to quickly reconfigure or repurpose, since it takes specialty knowledge to set up racks of robotic arms, computer vision systems, and so on. Robotics manufacturers are making advances in this field, but for now it’s a whole lot harder than training a human workforce to use standard tools on a different pattern.

In a press release, Adidas global operations head Martin Shankland explained that “The Speedfactories have been instrumental in furthering our manufacturing innovation and capabilities,” and that for a short time they even brought products to market in a hurry. “That was our goal from the start,” he says, though presumably things played out a bit differently in the pitch decks from 2016.

“We very much regret that our collaboration in Ansbach and Atlanta has come to an end,” Shankland said. Oechsler, the high-tech manufacturing partner that Adidas worked with, feels the same. “Whilst we understand adidas’ reasons for discontinuing Speedfactory production at Oechsler, we regret this decision,” said the company’s CEO, Claudius Kozlik, in the press release. The factories will shut down by April, presumably eliminating or shifting the 160 or so jobs they provided, but the two companies will continue to work together.

The release says that Adidas will “use its Speedfactory technologies to produce athletic footwear at two of its suppliers in Asia” starting next year. It’s not really clear what that means, and I’ve asked the company for further information.

 


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Watch MIT’s ‘mini cheetah’ robots frolic, fall, flip – and play soccer together

17:31 | 9 November

MIT’s Biomimetics Robotics department took a whole herd of its new ‘mini cheetah’ robots out for a group demonstration on campus recently – and the result is an adorable, impressive display of the current state of robotic technology in action.

The school’s students are seen coordinating the actions of 9 of the dog-sized robots running through a range of activities, including coordinated movements, doing flips, springing in slow motion from under piles of fall leaves, and even playing soccer.

The mini cheetah weights just 20 lbs, and its design was revealed for the first time earlier this year by a team of robot developers working at MIT’s Department of Mechanical Engineering. The mini cheetah is a shrunk-down version of the Cheetah 3, a much larger and more expensive to produce robot that is far less light on its feet, and not quite so customizable.

The mini cheetah was designed for Lego-like assembly using off-the-shelf part, as well as durability and relative low cost. It can walk both right-side up, and upside down, and its most impressive ability just might be the way it can manage a full backflip from a stand-still. It can also run at a speed of up to 5 miles per hour.

Researchers working on the robot set out to build a team of them after demonstrating that first version back in May, and are now working with other teams at MIT to loan them out for additional research.

 


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This robotic arm slows down to avoid the uncanny valley

00:48 | 8 November

Robotic arms can move fast enough to snatch thrown objects right out of the air… but should they? Not unless you want them to unnerve the humans they’re interacting with, according to work out of Disney Research. Roboticists there found that slowing a robot’s reaction time made it feel more normal to people.

Disney has of course been interested in robotics for decades, and the automatons in its theme parks are among the most famous robots in the world. But there are few opportunities for those robots to interact directly with people. Hence a series of research projects at its research division aimed at safe and non-weird robot-human coexistence.

In this case the question was how to make handing over an item to a robot feel natural and non-threatening. Obviously if, when you reached out with a ticket or empty cup, the robot moved like lightning and snapped it out of your hands, that could be seen as potentially dangerous, or at the very least make people nervous.

So the robot arm in this case (attached to an anthropomorphic cat torso) moves at a normal human speed. But there’s also the question of when it should reach out. After all, it takes us humans a second to realize that someone is handing something to us, then to reach out and grab it. A computer vision system might be able to track an object and send the hand after it more quickly, but it might feel strange.

The researchers set up an experiment where the robot hand reached out to take a ring from a person, under three conditions each of speed and delay.

When the hand itself moved quickly, people reported less “warmth” and more “discomfort.” The slow speed performed best on those scores. And hen the hand moved with no delay, it left people similarly uneasy. But interestingly, too long a delay had a similar effect.

Turns out there’s a happy medium that matches what people seem to expect from a hand reaching out to take something from them. Slower movement is better, to a certain point one imagines, and a reasonable but not sluggish delay makes it feel more human.

The handover system detailed in a paper published today (and video below) is robust against the usual circumstances: moving targets, unexpected forces, and so on. It’ll be a while before an Aristocats bot takes your mug from you at a Disney World cafe, but at least you can be sure it won’t snatch it faster than the eye can follow and scare everyone around you.

 


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Ghost wants to retrofit your car so it can drive itself on highways by 2020

21:01 | 7 November

A new autonomous vehicle company is on the streets — and unbeknownst to most, has been since 2017. Unlike the majority in this burgeoning industry, this new entrant isn’t trying to launch a robotaxi service or sell a self-driving system to suppliers and automakers. It’s not aiming for autonomous delivery, either.

Ghost Locomotion, which emerged Thursday from stealth with $63.7 million in investment from Keith Rabois at Founders Fund, Vinod Khosla at Khosla Ventures and Mike Speiser at Sutter Hill Ventures, is targeting your vehicle.

Ghost is developing a kit that will allow privately owned passenger vehicles to drive autonomously on highways. And the company says it will deliver in 2020.

This kit isn’t going to give a vehicle a superior advanced driving assistance system. The kit will let human drivers hand control of their vehicle over to a computer, allowing them to do other activities such as look at their phone or even doze off.

The idea might sound similar to what Comma.ai is working on, Tesla hopes to achieve or even the early business model of Cruise. Ghost CEO and co-founder John Hayes says what they’re doing is different.

A different approach

The biggest players in the industry — companies like Waymo, Cruise, Zoox and Argo AI — are trying to solve a really hard problem, which is driving in urban areas, Hayes told TechCrunch in a recent interview.

“It didn’t seem like anyone was actually trying to solve driving on the highways,” said Hayes, who previously founded Pure Storage in 2009. “At the time, we were told that this is so easy that surely the automakers will solve this any day now. And that really hasn’t happened.”

Hayes noted that automakers have continued to make progress in advanced driver assistance systems. The more advanced versions of these systems provide what the SAE describes as Level 2 automation, which means two primary control functions are automated. Tesla’s Autopilot system is a good example of this; when engaged, it automatically steers and has traffic-aware cruise control, which maintains the car’s speed in relation to surrounding traffic. But like all Level 2 systems, the driver is still in the loop.

Ghost wants to take the human out of the loop when they’re driving on highways.

“We’re taking, in some ways, a classic startup attitude to this, which is ‘what is the simplest product that we can perfect, that will put self driving in the hands of ordinary consumers?’ ” Hayes said. “And so we take people’s existing cars and we make them self-driving cars.”

The kit

Ghost is tackling that challenge with software and hardware.

The kit involves hardware like sensors and a computer that is installed in the trunk and connected to the controller area network (CAN) of the vehicle. The CAN bus is essentially the nervous system of the car and allows various parts to communicate with each other.

Vehicles must have a CAN bus and electronic steering to be able to use the kit.

The camera sensors are distributed throughout the vehicle. Cameras are integrated into what looks like a license plate holder at the back of the vehicle, as well as another set that are embedded behind the rearview mirror.

A third device with cameras is attached to the frame around the window of the door (see below).

Initially, this kit will be an aftermarket product; the company is starting with the 20 most popular car brands and will expand from there.

Ghost intends to set up retail spaces where a car owner can see the product and have it installed. But eventually, Hayes said, he believes the kit will become part of the vehicle itself, much like GPS or satellite radio has evolved.

While hardware is the most visible piece of Ghost, the company’s 75 employees have dedicated much of their time on the driving algorithm. It’s here, Hayes says, where Ghost stands apart.

How Ghost is building a driver

Ghost is not testing its self-driving system on public roads, an approach nearly every other AV company has taken. There are 63 companies in California that have received permits from the Department of Motor Vehicles to test autonomous vehicle technology (always with a human safety driver behind the wheel) on public roads.

Ghost’s entire approach is based on an axiom that the human driver is fundamentally correct. It begins by collecting mass amounts of video data from kits that are installed on the cars of high-mileage drivers. Ghost then uses models to figure out what’s going on in the scene and combines that with other data, including how the person is driving by measuring the actions they take.

It doesn’t take long or much data to model ordinary driving, actions like staying in a lane, braking and changing lanes on a highway. But that doesn’t “solve” self-driving on highways because the hard part is how to build a driver that can handle the odd occurrences, such as swerving, or correct for those bad behaviors.

Ghost’s system uses machine learning to find more interesting scenarios in the reams of data it collects and builds training models based on them.

The company’s kits are already installed on the cars of high-mileage drivers like Uber and Lyft drivers and commuters. Ghost has recruited dozens of drivers and plans to have its kits in hundreds of cars by the end of the year. By next year, Hayes says the kits will be in thousands of cars, all for the purpose of collecting data.

The background of the executive team, including co-founder and CTO Volkmar Uhlig, as well as the rest of their employees, provides some hints as to how they’re approaching the software and its integration with hardware.

Employees are data scientists and engineers, not roboticists. A dive into their resumes on LinkedIn and not one comes from another autonomous vehicle company, which is unusual in this era of talent poaching.

For instance, Uhlig, who started his career at IBM Watson Research, co-founded Adello and was the architect behind the company’s programmatic media trading platform. Before that, he built Teza Technologies, a high-frequency trading platform. While earning his PhD in computer science he was part of a team that architected the L4 Pistachio microkernel, which is commercially deployed in more than 3 billion mobile Apple and Android devices.

If Ghost is able to validate its system — which Hayes says is baked into its entire approach — privately owned self-driving cars could be on the highways by next year. While the National Highway Traffic Safety Administration could potentially step in, Ghost’s approach, like Tesla, hits a sweet spot of non-regulation. It’s a space, that Hayes notes, where the government has not yet chosen to regulate.

 


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MIT develops a robot that can grow like a plant when it needs some extra reach

20:57 | 7 November

MIT has developed a new kind of robot that can essentially extend itself – ‘growing’ in a way that’s surprisingly similar to how a young plant grows upwards. The way researchers accomplished this uses a robot that is crucially not a soft robot, which means that it can both extend itself to reach up to a previously unreachable height, or through a gap to a hard-to-get-at area, while also retaining the rigidity and strength necessary to support a gripper or other mechanism on its tip and do work like tighten a bolt, or manipulate a handle.

This new robot solves a fairly common challenge for industrial and commercial robots, which is reaching into tight spaces or navigating cluttered parts of factories or warehouses. Most robots that are in service in industry today essentially need a wide open space to operate, and factory layouts are designed to provide these to accommodate them. They’re also essentially fixed in terms of their dimensions: It’s rare to find a robot that can truly morph to meet the needs of different tasks.

As mentioned, soft robotics has done some work in addressing the fixed nature of existing robots, but to date, not much that’s been done with soft robotics can also accommodate the need for robots to work with what are called in robotics “end effectors,” or at least the variety that includes grippers and heavy duty cameras or sensors, which tend to have a significant weight and require a stable platform from which to operate.

MIT’s robot solves this using a chain-like apparatus, not too different from a bicycle chain. The difference is that its links are interlocking blocks that can ‘lock’ into one another to form a rigid column, and then ‘unlock’ to return to a flexible state. This means a robot could pack this chain-like appendage, loose in a container, drive across a factory floor to a bit of machinery, and then extend a growing ‘arm’ inside the machinery where it otherwise wouldn’t be able to reach to manipulate a control, or to point a sensor to check for damage, for instance.

Roboticists have termed this the ‘last foot’ problem – similar in nature to the ‘last mile’ in transportation, since it involves going from a wide, relatively easy to access space to a more difficult to reach area. In autonomous vehicles, that might be getting from the curb to a person’s doorstep. In industrial robotics, it’s about going from working out in the open to working in tight, confined spaces.

Unlocking this kind of flexibility for industrial robotics could go a long way to opening up entirely new and more varied applications of robots on the job, so the development of these kinds of ‘grobots’ definitely has plenty of potential outside the lab.

 


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Volocopter and John Deere team up for a crop-spraying autonomous agricultural drone

17:27 | 7 November

Autonomous drone-based transportation startup Volocopter has revealed its first partner for its new VoloDrone industrial and commercial electric vertical take-off and landing craft: John Deere . The agricultural and industrial heavy equipment company is working with Volocopter on a VoloDrone-based aerial crop-dusting system.

VoloDrone, which Voloctoper unveiled at the end of last month, has 18 rotors and a fully electric power system that can provide up to 30 minutes of flight time for the aircraft, and carry up to 440 lbs. It’s designed to operate fully autonomously along a set path, or be piloted remotely for manual control if needed. John Deere is equipping the VoloDrone with a sprayer and tank array mounted to its cargo connection points, which will be able to dispense pesticides, liquid fertilizer, anti-frost agents for unseasonable inclement weather and more. Both partners also see potential in applications like sowing seeds for new crops from the air.

The VoloDrone is potentially a better, more precise and more cost-effective alternative to using a helicopter for these applications, Volocopter says. They’ll be working with John Deere to test and prove that out with initial pilots to be conducted during the next agricultural growing season.

 


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Yandex is now testing a self-driving sidewalk cargo delivery robot

16:21 | 7 November

Add another one to the list of companies piloting small wheeled autonomous robots for small package and food delivery: Yandex . Russia’s search and services giant has expanded its ambitions in the world of autonomous transportation, building on its work with self-driving cars to deploy a six-wheeled robot that adopts the popular cooler on wheels style pioneered by Starship Robotics.

The small autonomous robot, appropriately dubbed ‘Rover,’ has a suite of sensors, including that prominent lidar array on top, and it moves at “average walking speed” according to the company. It includes software that can help it avoid people walking in its path, pets, and just about any other objects that might block the sidewalk while it’s in motion on its way to its destination.

The initial pilot includes testing on the main Yandex corporate campus in Moscow, across a range of weather conditions, and during both the day and at night. The Moscow HQ hosts over 7,000 employees, and spans both office buildings, restaurants and parking garages. Ynadex is providing food deliveries and groceries from its own Yandex.Eats and Yandex.Lavka platforms, respectively, and also small goods transportation is another area of potential expansion, since Yandex owns and operates its own e-commerce platform Beru. The company also says Rover could find a home within its warehouses and data centres for in-office transportation.

That’s what most differentiates the Yandex wheeled delivery bot from most of the other ones current in service or in development: At Yandex, there are a lot of other businesses in-house that could benefit from autonomous last-mile transportation. Companies like Postmates and Amazon also have primary businesses that benefit, while Starship and other dedicated companies need to sell to clients to stand up their revenue generation. Yandex might be unique in the breadth of in-house businesses for which an autonomous wheeled small parcel robot could have knock-on benefits.

 


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Amazon is planning a $40M robotics hub near Boston

18:46 | 6 November

Amazon announced a plan today to build a $40 million, 350,000 square foot robotics innovation hub in Westborough, MA. The new facility will bring 200 technology and advanced manufacturing jobs when it opens in 2021.

The new facilities will include corporate offices, research and development labs and a robotics manufacturing space. The company said that this new facility will be in addition to its existing Amazon Robotics site in North Reading, MA.

Tye Brady, chief technologist at Amazon Robotics says the new hub will allow Amazon to continue to build on its robotics research. “This will be a world-class facility, where our teams can design, build, program and ship our robots — all under the same roof. This expansion will allow us to continue to innovate quickly and improve delivery speed for customers around the world,” Brady said in a statement.

Amazon bought Kiva Systems, a company that was developing order fulfillment robots, in 2012 for $775 million in cash, and then renamed the company Amazon Robotics. A lot has changed since 2012, and today the company has deployed more than 100,000 robotic systems in more than 25 fulfillment centers across the U.S, and has more than double that in place worldwide including both Amazon and third-party usage, according to data supplied by the company.

Amazon is not new to Massachusetts. It has had a presence in the Bay State since 2011 has invested over $3 billion and created more than 4,000 full-time jobs in Massachusetts, according to data supplied by the company.

Westborough is a suburb located about 35 miles west of Boston.

 


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MIT develops a way for autonomous delivery robots to find your front door

22:12 | 4 November

Researchers at MIT have developed a new method of navigation for robots that could be very useful for the range of companies working on autonomous last-mile delivery. In short, the team has worked out how a robot can figure out the location of a front door, without being provided a specific map in advance.

Most last-mile autonomous delivery robots today, including the ‘wheeled cooler’-style variety that was pioneered by Starship and has since been adopted by a number of other companies, including Postmates, basically meet customers at the curb. Mapping isn’t the only barrier to having future delivery bots go all the way to the door, just like the humans who make those deliveries today.

MIT News points out that mapping an entire neighborhood with the level of specificity required to do true front-door delivery would be incredibly difficult – particularly at national (let alone global) scale. Since that seems unlikely to happen, and especially unlikely for every company looking at building autonomous delivery networks to source separately, they set out to devise a navigation method that lets a robot process cues in its surroundings on the fly to figure out a front door’s location.

This is a variation on what you may have heard of referred to as SLAM, or simultaneous localization and mapping. The MIT team’s innovative twist on this approach is that in place of a semantic map, wherein the robot identifies objects in its surroundings and labels them, they devised a ‘cost-to-go’ map, which uses data from training maps to color-code the surroundings into a heat map wherein it can determine which parts are more likely to be close to a ‘front door’ and which are not, and immediately chart the most efficient path to the door based on that info.

It’s a much, much more simplified version of what we do when we encounter new environments we’ve never seen directly before – you know what’s likely to be the front door of a house you’ve never seen just by looking at it, and you know that essentially because you’re comparing it against your memory of past houses and how those properties have been laid out, even if you’re doing that all without even thinking about it.

Delivery is only one use case for this kind of intelligent local environment mapping, but it’s a good one that might see actual commercial use sooner rather than later.

 


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