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Toyota Research Institute’s robots leave home



“I think I’m probably just as guilty as everybody else,” Toyota Research Institute’s (TRI) senior vice president of robotics, Max Bajracharya, admits. “It’s like, now our GPUs are better. Oh, we got machine learning and now you know we can do this. Oh, okay, maybe that was harder than we thought.”

Ambition is, of course, an important aspect of this work. But there’s also a grand, inevitable tradition of relearning mistakes. The smartest people in the room can tell you a million times over why a specific issue hasn’t been solved, but it’s still easy to convince yourself that this time — with the right people and the right tools — things will just be different.

In the case of TRI’s in-house robotics team, the impossible task is the home. The lack of success in the category hasn’t been for lack of trying. Generations of roboticists have agreed that there are plenty of problems waiting to be automated, but thus far, successes have been limited. Beyond the robotic vacuum, there’s been little in the way of breakthrough.

TRI’s robotics team has long made the home a primary focus. That’s driven, in no small part, by it choosing eldercare as a “north star” for the same reason that Japanese firms are so far ahead of the rest of the world in the category. Japan has the world’s highest proportion of citizens over the age of 65 — trailing only Monaco, a microstate in Western Europe with a population of fewer than 40,000.

In a world where our health and wellness are so closely tied to our ability to work, it’s an issue bordering on crisis. It’s the kind of thing that gets Yale assistant professors New York Times headlines for suggesting mass suicide. That’s obviously the most sensationalistic of “solutions,” but it’s still an issue in search of meaningful solution. As such, many Japanese roboticists have turned to robotics and automation to address issues like at-home healthcare, food preparation and even loneliness.

Image Credits: Brian Heater

Early, professionally produced videos showcased robotics in the home, executing complex tasks, like cooking and cleaning a broad range of surfaces. When TRI opened the doors of its South Bay labs to select press this week to show off a range of its different projects, the home element was notably lacking. Bajracharya showcased a pair of robots. The first was a modified off-the-shelf arm that moved boxes from a pile onto nearby conveyer belts, in a demo designed for unloading trucks — one of the more difficult tasks to automate in an industrial warehouse setting.

The second was a wheel robot that goes shopping. Unlike the warehouse example, which had standard parts with a modified gripper, this system was largely designed in-house out of necessity. The robot is sent out to retrieve different products on the shelf based on bar codes and general location. The system is able to extend to the top shelf to find items, before determining the best method for grasping the broad range of different objects and dropping them into its basket. The system is an outgrowth of the team’s pivot away from home-specific robots.

Image Credits: Brian Heater

To the side of both robots is a mock kitchen, with a gantry system configured to the top of its walls. A quasi-humanoid robot hangs down, immobile and lifeless. It goes unacknowledged for the duration of the demos, but the system will look familiar to anyone who has watched the team’s early concept videos.

“The home is so hard,” says Bajracharya. “We pick challenge tasks because they are hard. The problem with the home is not that it was too hard. It was that it was too hard to measure the progress we were making. We tried a lot of things. We tried procedurally making a mess. We would put flour and rice on the tables and we would try to wipe them up. We would put things throughout the house to make the robot tidy. We were deploying into Airbnbs to see how well we were doing, but the problem is we couldn’t get the same home every time. But if we did, we would overfit to that home.”

Moving into the supermarket was an effort to address a more structured environment while still tackling a pressing issue for the elderly community. In testing the product, the team has moved from Airbnbs to a local mom-and-pop grocery store.

Image Credits: Brian Heater

“To be totally honest, the challenge problem kind of doesn’t matter,” Bajracharya explains. “The DARPA Robotics Challenges, those were just made up tasks that were hard. That’s true of our challenge tasks, too. We like the home because it is representative of where we eventually want to be helping people in the home. But it doesn’t have to be the home. The grocery market is a very good representation because it has that huge diversity.”

In this instance, some of the learnings presented in this setting do translate to Toyota’s broader needs.

What, precisely, constitutes progress for a team of this nature is a difficult question to answer. It’s certainly one that’s top of mind, however, as large corporations have begun cutting roles in longtail research projects that have yet to deliver tangible, monetizable results. When I put the question to Gill Pratt yesterday, the TRI boss told me:

Toyota is a company that has tried very hard not to have employment follow business cycle. The car business is one that has booms and busts all the time. You may know that the history of Toyota is to try not to lay people off when times are tough, but instead go through a couple of things. One is shared sacrifice, where people take up the cause. The second is to use those times to invest in maintenance, plans and education to help people get trained.

Image Credits: Brian Heater

Toyota is well-known in the industry for its “no layoffs” policy. It’s an admirable goal, certainly, especially as companies like Google and Amazon are in the midst of layoffs numbering in the tens of thousands. But when goals are more abstract, as is the case with TRI and fellow research wings, how does a company measure relevant milestones?

“We were making progress on the home but not as fast and not as clearly as when we move to the grocery store,” the executive explains. “When we move to the grocery store, it really becomes very evident how well you’re doing and what the real problems are in your system. And then you can really focus on solving those problems. When we toured both logistics and manufacturing facilities of Toyota, we saw all of these opportunities where they’re basically the grocery shopping challenge, except a little bit different. Now, instead of the parts being grocery items, the parts are all the parts in a distribution center.”

As is the nature of research projects, Bajracharya adds, sometimes the beneficial outcomes are unexpected: “The projects are still looking at how we ultimately amplify people in their homes. But over time, as we pick these challenge tasks, if things trickle out that are applicable to these other areas, that’s where we’re using these short-term milestones to show the progress in the research that we’re making.”

The path toward productizing such breakthroughs can also be fuzzy sometimes.

“I believe we kind of understand the landscape now,” Bajracharya. “Maybe I was naive in the beginning thinking that, okay, we just need to find this person that we’re going to throw the technology over to a third party or somebody inside of Toyota. But I think what we’ve learned is that, whatever it is — whether it’s a business unit, or a company, or like a startup or a unit inside of Toyota — they don’t seem to exist.”

Spinning out startups — akin to what Alphabet has done with its X labs — is certainly on the table, even though it isn’t likely to be the primary path toward productization. What form that path will ultimately take, however, remains unclear. Though robotics as a category is currently far more viable than it was when TRI was founded in 2017.

“Over the last five years, I feel like we’ve made enough progress in that very challenging problem that we are now starting to see it turn into these real-world applications,” says Bajracharya. “We have consciously shifted. We’re still 80% pushing the state of the art with research, but we’ve now allocated maybe 20% of our resources to figuring out if that research is maybe as good as we think it is and if it can be applied to real-world applications. We might fail. We might realize we thought we made some interesting breakthroughs, but it’s not anywhere near reliable or fast enough. But we’re putting 20% of our effort toward trying.”


Tesla more than tripled its Austin gigafactory workforce in 2022



Tesla’s 2,500-acre manufacturing hub in Austin, Texas tripled its workforce last year, according to the company’s annual compliance report filed with county officials. Bloomberg first reported on the news.

The report filed with Travis County’s Economic Development Program shows that Tesla increased its Austin workforce from just 3,523 contingent and permanent employees in 2021 to 12,277 by the end of 2022. Bloomberg reports that just over half of Tesla’s workers reside in the county, with the average full-time employee earning a salary of at least $47,147. Outside of Tesla’s factory, the average salary of an Austin worker is $68,060, according to data from ZipRecruiter.

TechCrunch was unable to acquire a copy of the report, so it’s not clear if those workers are all full-time. If they are, Tesla has hired a far cry more full-time employees than it is contracted to do. According to the agreement between Tesla and Travis County, the company is obligated to create 5,001 new full-time jobs over the next four years.

The contract also states that Tesla must invest about $1.1 billion in the county over the next five years. Tesla’s compliance report shows that the automaker last year invested $5.81 billion in Gigafactory Texas, which officially launched a year ago at a “Cyber Rodeo” event. In January, Tesla notified regulators that it plans to invest another $770 million into an expansion of the factory to include a battery cell testing site and cathode and drive unit manufacturing site. With that investment will come more jobs.

Tesla’s choice to move its headquarters to Texas and build a gigafactory there has helped the state lead the nation in job growth. The automaker builds its Model Y crossover there and plans to build its Cybertruck in Texas, as well. Giga Texas will also be a model for sustainable manufacturing, CEO Elon Musk has said. Last year, Tesla completed the first phase of what will become “the largest rooftop solar installation in the world,” according to the report, per Bloomberg. Tesla has begun on the second phase of installation, but already there are reports of being able to see the rooftop from space. The goal is to generate 27 megawatts of power.

Musk has also promised to turn the site into an “ecological paradise,” complete with a boardwalk and a hiking/biking trail that will open to the public. There haven’t been many updates on that front, and locals have been concerned that the site is actually more of an environmental nightmare that has led to noise and water pollution. The site, located at the intersection of State Highway 130 and Harold Green Road, east of Austin, is along the Colorado River and could create a climate catastrophe if the river overflows.

The site of Tesla’s gigafactory has also historically been the home of low-income households and has a large population of Spanish-speaking residents. It’s not clear if the jobs at the factory reflect the demographic population of the community in which it resides.

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Launch startup Stoke Space rolls out software tool for complex hardware development



Stoke Space, a company that’s developing a fully reusable rocket, has unveiled a new tool to let hardware companies track the design, testing and integration of parts. The new tool, Fusion, is targeting an unsexy but essential aspect of the hardware workflow.

It’s a solution born out of “ubiquitous pain in the industry,” Stoke CEO Andy Lapsa said in a recent interview. The current parts tracking status quo is marked by cumbersome, balkanized solutions built on piles of paperwork and spreadsheets. Many of the existing tools are not optimized “for boots on the ground,” but for finance or procurement teams, or even the C-suite, Lapsa explained.

In contrast, Fusion is designed to optimize simple inventory transactions and parts organization, and it will continue to track parts through their lifespan: as they are built into larger assemblies and go through testing. In an extreme example, such as hardware failures, Fusion will help teams connect anomalous data to the exact serial numbers of the parts involved.

Image credit: Stoke Space

“If you think about aerospace in general, there’s a need and a desire to be able to understand the part pedigree of every single part number and serial number that’s in an assembly,” Lapsa said. “So not only do you understand the configuration, you understand the history of all of those parts dating back to forever.”

While Lapsa clarified that Fusion is the result of an organic in-house need for better parts management – designing a fully reusable rocket is complicated, after all – turning it into a sell-able product was a decision that the Stoke team made early on. It’s a notable example of a rocket startup generating pathways for revenue while their vehicle is still under development.

Fusion offers particular relevance to startups. Many existing tools are designed for production runs – not the fast-moving research and development environment that many hardware startups find themselves, Lapsa added. In these environments, speed and accuracy are paramount.

Brent Bradbury, Stoke’s head of software, echoed these comments.

“The parts are changing, the people are changing, the processes are changing,” he said. “This lets us capture all that as it happens without a whole lot of extra work.”

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Amid a boom in AI accelerators, a UC Berkeley-focused outfit, House Fund, swings open its doors



Companies at the forefront of AI would naturally like to stay at the forefront, so it’s no surprise they want to stay close to smaller startups that are putting some of their newest advancements to work.

Last month, for example, Neo, a startup accelerator founded by Silicon Valley investor Ali Partovi, announced that OpenAI and Microsoft have offered to provide free software and advice to companies in a new track focused on artificial intelligence.

Now, another Bay Area outfit — House Fund, which invests in startups with ties to UC Berkeley — says it is launching an AI accelerator and that, similarly, OpenAI, Microsoft, Databricks, and Google’s Gradient Ventures are offering participating startups free and early access to tech from their companies, along with mentorship from top AI founders and executives at these companies.

We talked with House Fund founder Jeremy Fiance over the weekend to get a bit more color about the program, which will replace a broader-based accelerator program House Fund has run and whose alums include an additive manufacturing software company, Dyndrite, and the managed app development platform Chowbotics, whose most recent round in January brought the company’s total funding to more than $60 million.

For founders interested in learning more, the new AI accelerator program runs for two months, kicking off in early July and ending in early September. Six or so companies will be accepted, with the early application deadline coming up next week on April 13th. (The final application deadline is on June 1.) As for the time commitment involved across those two months, every startup could have a different experience, says Fiance. “We’re there when you need us, and we’re good at staying out of the way.”

There will be the requisite kickoff retreat to spark the program and founders to get to know one another. Candidates who are accepted will also have access to some of UC Berkeley’s renowned AI professors, including Michael Jordan, Ion Stoica, and Trevor Darrell. And they can opt into dinners and events in collaboration with these various constituents.

As for some of the financial dynamics, every startup that goes through the program will receive a $1 million investment on a $10 million post-money SAFE note. Importantly, too, as with the House Fund’s venture dollars, its AI accelerator is seeking startups that have at least one Berkeley-affiliated founder on the co-founding team. That includes alumni, faculty, PhDs, postdocs, staff, students, dropouts, and other affiliates.

There is no demo day. Instead, says Fiance, founders will receive “directed, personal introductions” to the VCs who best fit with their startups.

Given the buzz over AI, the new program could supercharge House Fund, the venture organization, which is already growing fast. Fiance launched it in 2016 with just $6 million and it now manages $300 million in assets, including on behalf of Berkeley Endowment Management Company and the University of California.

At the same time, the competition out there is fierce and growing more so by the day.

Though OpenAI has offered to partner with House Fund, for example, the San Francisco-based company announced its own accelerator back in November. Called Converge, the cohort was to be made up of 10 or so founders who received $1 million each and admission to five weeks of office hours, workshops and other events that ended and that received their funding from the OpenAI Startup Fund.

Y Combinator, the biggest accelerator in the world, is also oozing with AI startups right now, all of them part of a winter class that will be talking directly with investors this week via demo days that are taking place tomorrow, April 5th, and on Thursday.

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