Though Spotify never shared official numbers, it’s likely that Car Thing underperformed or was just not worth continued investment in today’s tighter economic market.
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Spotify is notifying customers who purchased its Car Thing product that the devices will stop working after December 9, 2024. The company discontinued the device back in July 2022, but had said at the time that it would still support the units that had already been sold. The company’s plans for the device have changed, […]
© 2024 TechCrunch. All rights reserved. For personal use only.
Wander the pits at any professional motorsports event, especially something like Formula 1, and you’ll see endless computer displays full of telemetry. Modern teams are awash in real-time digital feedback from the cars. I’ve been in many of these pits over the years and marveled at the streams of data, but never have I seen an instance of the Microsoft Visual Studio software development suite running there right amid the chaos.
But then, I’ve never attended anything like the inaugural Abu Dhabi Autonomous Racing League event this past weekend. The A2RL, as it is known, is not the first autonomous racing series: There’s the Roborace series, which saw autonomous race cars setting fast lap times while dodging virtual obstacles, and the Indy Autonomous Challenge, which most recently ran at the Las Vegas Motor Speedway during CES 2024.
While the Roborace focuses on single-car time trials and the Indy Autonomous series centers on oval action, A2RL set out to break new ground in a couple of areas.
A2RL put four cars on track, competing simultaneously for the first time. And, perhaps more significantly, it pitted the top-performing autonomous car against a human being, former Formula 1 pilot Daniil Kvyat, who drove for various teams between 2014 and 2020.
The real challenge was behind the scenes, with teams staffed with an impressively diverse cadre of engineers, ranging from fledgling coders to doctorate students to full-time race engineers, all fighting to find the limit in a very new way.
Unlike Formula 1, where 10 manufacturers design, develop and produce completely bespoke cars (sometimes with the help of AI), the A2RL race cars are entirely standardized to provide a level playing field. The 550-horsepower machines, borrowed from the Japanese Super Formula Championship, are identical, and the teams are not allowed to change a single component.
That includes the sensor array, which features seven cameras, four radar sensors, three lidar sensors and GPS to boot — all of which are used to perceive the world around them. As I would learn while wandering the pits and chatting to the various teams, not everybody is fully tapping into the 15 terabytes of data each car hoovers up every single lap.
Some teams, like the Indianapolis-based Code19, only started work on the monumental project of creating a self-driving car a few months ago. “There’s four rookie teams here,” said Code19 co-founder Oliver Wells. “Everyone else has been competing in competitions just like this, some of them for up to seven years.”
Munich-based TUM and Milan-based Polimove have extensive experience running and winning in both Roborace and the Indy Autonomous Challenge. That experience carries over, as does the source code.
“On the one hand, the code is continuously developed and improved anyway,” said Simon Hoffmann, team principal at TUM. The team made adjustments to change the cornering behavior to suit the sharper turns in the road course and also adjust the overtaking aggression. “But in general, I would say we use the same base software,” he said.
Through the series of numerous qualifying rounds throughout the weekend, the teams with the greatest experience dominated the timing charts. TUM and Polimove were the only two teams to complete lap times in less than two minutes. Code19’s fastest lap, however, was just over three minutes; the other new teams were far slower.
This has created a competition that’s rarely seen in software development. While there have certainly been previous competitive coding challenges, like TopCoder or Google Kick Start, this is a very different sort of thing. Improvements in code mean faster lap times — and fewer crashes.
Kenna Edwards is a Code19 assistant race engineer and a student at Indiana University. She brought some previous app development experience to the table, but had to learn C++ to write the team’s antilock braking system. “It saved us at least a couple of times from crashing,” she said.
Unlike traditional coding problems that might require debuggers or other tools to monitor, improved algorithms here have tangible results. “A cool thing has been seeing the flat spots on the tire improve over the next session. Either they’ve reduced in size or in frequency,” Edwards said.
This implementation of theory not only makes for engaging engineering challenges but also opens up viable career paths. After earlier interning with Chip Ganassi Racing and General Motors, and thanks to her experience with Code19, Edwards starts full-time at GM Motorsports this summer.
That sort of development is a huge part of what A2RL is about. Shadowing the main on-track action is a secondary series of competitions for younger students and youth groups around the world. Before the main A2RL event, those groups competed with autonomous 1:8-scale model cars.
“The aim is, next year, we keep for the schools the smaller model cars, we’ll keep for the universities maybe doing it on go-karts, a bit bigger, they can play with the autonomous go-karts. And then, if you want to be in the big league, you start racing on these cars,” said Faisal Al Bannai, the secretary general of Abu Dhabi’s Advanced Technology Research Council, the ATRC. “I think by them seeing that path, I think you’ll encourage more guys to come into research, to come into science.”
It’s Al Bannai’s ATRC that’s footing the bill for the A2RL, covering everything from the cars to the hotels for the numerous teams, some of whom have been testing in Abu Dhabi for months. They also put on a world-class party for the main event, complete with concerts, drone races, and a ridiculous fireworks show.
The on-track action was a little less spectacular. The first attempt at a four-car autonomous race was aborted after one car spun, blocking the following cars. The second race, however, was far more exciting, featuring a pass for the lead when the University of Modena’s Unimore team car went wide. It was TUM that made the pass and won the race, taking home the lion’s share of the $2.25 million prize purse.
As for man vs. machine, Daniil Kvyat made quick work of the autonomous car, passing it not once but twice to huge cheers from the assembled crowd of more than 10,000 spectators who took advantage of free tickets to come see a little bit of history — plus around 600,000 more streaming the event.
The technical glitches were unfortunate. Still it was a remarkable event to witness and illustrated how far autonomy has come — and of course, how much more progress needs to be made. The fastest car was still upward of 10 seconds off of Kvyat’s time. However, it ran smooth, clean laps at an impressive speed. That’s in stark contrast to the first DARPA Grand Challenge in 2004, which saw every single competitor either crashing into a barrier or meandering off into the desert on an unplanned sojourn.
For A2RL, the real test will be whether it can evolve into a financially viable series. Advertising drives most motorsports, but here, there’s the added benefit of developing algorithms and technologies that manufacturers could reasonably apply in their cars.
ATRC’s Al Bannai told me that while the series organizers own the cars, the teams own the code and are free to license it: “What they compete on at the moment is the algorithm, the AI algorithm that makes this car do what it does. That belongs to each of the teams. It doesn’t belong to us.”
The real race, then, might not be on the track, but in securing partnerships with manufacturers. After all, what better way to inspire confidence in your autonomous technology than by showing it can handle traffic on the race track at 160 mph?
Sanctuary AI announced that it will be delivering its humanoid robot to a Magna manufacturing facility. Based in Canada, with auto manufacturing facilities in Austria, Magna manufactures and assembles cars for a number of Europe’s top automakers, including Mercedes, Jaguar and BMW. As is often the nature of these deals, the parties have not disclosed how many of Sanctuary AI’s robots will be deployed.
The news follows similar deals announced by Figure and Apptronik, which are piloting their own humanoid systems with BMW and Mercedes, respectively. Agility also announced a deal with Ford at CES in January 2020, though that agreement found the American carmaker exploring the use of Digit units for last-mile deliveries. Agility has since put that functionality on the back burner, focusing on warehouse deployments through partners like Amazon.
For its part, Magna invested in Sanctuary AI back in 2021 — right around the time Elon Musk announced plans to build a humanoid robot to work in Tesla factories. The company would later dub the system “Optimus.” Vancouver-based Sanctuary unveiled its own system, Phoenix, back in May of last year. The system stands 5’7” (a pretty standard height for these machines) and weighs 155 pounds.
Phoenix isn’t Sanctuary’s first humanoid (an early model had been deployed at a Canadian retailer), but it is the first to walk on legs — this is in spite of the fact that most available videos only highlight the system’s torso. The company has also focused some of its efforts on creating dexterous hands — an important addition if the system is expected to expand functionality beyond moving around totes.
Sanctuary calls the pilot, “a multi-disciplinary assessment of improving cost and scalability of robots using Magna’s automotive product portfolio, engineering and manufacturing capabilities; and a strategic equity investment by Magna.”
As ever, these agreements should be taken as what they are: pilots. They’re not exactly validation of the form factor and systems — that comes later, if Magna gets what it’s looking for with the deal. That comes down to three big letters: ROI.
The company isn’t disclosing specifics with regard to the number of robots, the length of the pilot or even the specific factory where they will be deployed.
Apple is laying off 614 employees in California after abandoning its electric car project. According to the WARN notice posted by the California Employment Development Department, Apple notified the affected employees on March 28 and the changes will go into effect on May 27. Affected employees worked at eight locations in Santa Clara, roughly 45 miles south of San Francisco.
Although the notice doesn’t specify which projects the employees were working on, Bloomberg reports that most of the affected employees were working at buildings related to its canceled car project, while others were working at a facility for its next-generation screen development.
Apple wound down both of these projects toward the end of February. The company started working on its car project, known internally as “Project Titan,” in 2014, and told employees that it was canceling it on February 27. Bloomberg reported at the time that some remaining employees who were working on the car project would be shifted to Apple’s generative AI projects.
Around the same time, Apple reportedly ended efforts to design and develop its own next-generation displays. The displays were supposed to be added to the company’s Apple Watch before potentially going into the company’s other devices.
The layoffs mark Apple’s first major round of job cuts post-pandemic.
Apple did not immediately respond to a request for comment.
For every tech success story, there are countless projects that slam headlong into the brick wall of reality. Apple’s electric vehicle ambitions are one of the most recent — and, frankly, best — examples of a project failing in spite of seeming to have everything going for it.
The jury is still out on the ultimate fate of the Vision Pro, but at the very least, Apple’s mixed reality headset demonstrates that the company isn’t afraid to keep trying where pretty much everyone else has failed. With the Apple Car firmly in the rearview, the company is reportedly exploring yet another notoriously difficult path: home robots.
The category is both unique and uniquely difficult for a number of reasons. One thing that sets it apart from other categories is the fact that there’s been precisely one success story: the robot vacuum. It’s been 22 years since the first Roomba was introduced, and for the past two decades, an entire industry (including iRobot itself) has been chasing that success.
iRobot’s inability to strike gold a second time is not for lack of trying. In the nearly quarter-century since it introduced Roomba, it’s given us gutter clearers, pool cleaners, lawn mowers and even a Roomba specifically designed to remove screws and other hardware detritus off garage floors. In spite of those efforts, however, the company has fared best when it focused its resources back into its robot vacuum.
Image Credits: iRobot
The robot vacuum succeeded for the same reason any robot has ever succeeded: It was a product built to perform a single in-demand task repetitively to the best of its ability. To this day, vacuums are the battlefield on which the home robot wars are fought. Take the well-funded Bay Area startup Matic. The former Google/Nest engineers who founded the company believe the next breakthrough in the home will be built on the foundation of robot vacuums. Their case, in part, is that iRobot effectively painted itself into a corner with its puck-like form factor.
Those early Roombas weren’t built with today’s sensing and mapping capabilities in mind. Matic believes that by simply making the robot taller, you dramatically improve its vantage point. This was also the driver behind the most interesting innovation found on Amazon’s Astro home robot: the periscope camera.
Image Credits: Amazon
The fact is that home robot functionality is severely hampered by form factor. The hockey puck design that’s prevalent across robot vacuums isn’t ideal for anything beyond the core functionality it’s built for. To effectively perform more of the sorts of tasks people might desire in a home robot, the hardware needs to get more complex. Mobile manipulators are a great moving target. That is to say, if you want a helping hand, a hand is a good place to start.
Like so many other things in this world, however, mobile manipulators are deceptively difficult. In fact, industrial robotics haven’t cracked it yet. Big, bolted-down arms are common in manufacturing, and wheeled autonomous mobile robots (AMRs) like Locus and Kiva are common in warehouses, but the middle ground between the two hasn’t been firmly established. This is a big part of the reason the human element remains important in that world. It’s a problem that will be solved soon enough, but it seems likely it will happen with these more expensive industrial machines well before it makes its way into more affordable home robots (as a rule, corporations generally have deeper pockets than people).
This is also a big part of the reason many are championing the humanoid form factor in the workplace (human beings, after all, offer a kind of mobile manipulation). But that’s a longwinded think piece for another day.
Image Credits: Hello Robotics
Mobile manipulation isn’t entirely out of reach for home robots. Hello Robot’s Stretch is probably the most compelling example at the moment. Rather than a humanoid form factor, the robot looks like a Roomba with a pole mounted in its center. This houses both an imaging system and an arm that moves up and down to clasp objects (dishes, laundry) at different heights. Of course, some tasks are more easily accomplished with two arms — and suddenly you start to see why so many robotics firms have effectively backward-engineered humanoids.
In its current form, Stretch is prohibitively expensive at $24,950. That’s likely a big part of the reason the company is selling it as a development platform. Interestingly, Matic sees its own robot as a kind of development platform — using vacuuming as a gateway into additional home chores.
Another issue with Stretch is that it’s teleoperated (the company sent us a note after this published stating that some developers have created autonomous functions). There’s nothing wrong with teleop in many scenarios, but it seems unlikely that people are going to flock to a home robot that’s being controlled by a human somewhere far away.
Navigation is another key barrier to the home. Compared to warehouses and factories, homes are relatively unstructured environments. They differ greatly from one to another, lighting tends to be all over the place and humans are constantly moving stuff around and dropping things on the floor.
Matic’s vacuum uses an array of cameras to map spaces — and understand where it is in them. Image Credits: Matic
The world of self-driving has faced its own obstacles on this front. But the key difference between an autonomous robot on the highway and another in the home is that the worst the latter is probably going to do is knock something off a shelf. That’s bad, but very rarely does it result in death. With self-driving cars, on the other hand, any accident represents a significant step back for the industry. The technology is — perhaps understandably — being held to a higher standard than its human counterpart.
While adoption of self-driving technologies is well behind the curve that many anticipated, largely for the above safety reason, many of the technologies developed for the category have helped quietly kickstart their own robotics revolution, as autonomous vehicles take over farms and sidewalks.
This is likely a big part of the reason it might view home robots as “the next big thing” (to quote Bloomberg quoting its sources). Apple has no doubt pumped a tremendous amount of resources into driving technologies. If those could be repurposed for a different project, maybe it won’t all be for naught.
While the reports note that Apple “hasn’t committed” to either the robotic smart screen or mobile robot that are said to exist somewhere inside the company’s skunkworks, it has already put Apple Home execs Matt Costello and Brian Lynch on the hardware side of things, while SVP of Machine Learning and AI Strategy John Giannandrea is said to be involved on the AI side of things.
Image Credits: Brian Heater
Given the proximity to its home efforts, one can imagine the company working on its own version of Amazon’s Astro — though that project currently exists as more of a cautionary tale for the time being. The project has been hamstrung by high cost and a lack of useful features to justify it. The system also effectively served as a mobile Alexa portal, and home assistants have largely fallen out of fashion of late.
Apple does have some robotics expertise — though nothing approaching what Amazon has on its industrial side. The company has been involved in the production of robot arms like Daisy, which salvages key metals from discarded iPhones. That’s still a pretty large leap to a home robot.
Perhaps the company could take a more Vision Pro-like approach to the category, which has a heavy focus on developer contributions. Doing so, however, would require an extremely versatile hardware platform, which would almost certainly be cost-prohibitive for most consumers, making the Vision Pro’s $3,500 price tag look like small potatoes.
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