Co-founder and CEO @atlasmotion
Long Beach, CA
Joined November 2013
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I'm grateful to share that we've raised an $11.5M seed, led by amazing folks at Greycroft and Also Capital @nunzi46 .
The team has moved at a blistering pace over the last 5 months, and I’m excited to share more with the world on where we see this going.
Introducing Atlas
We're building motion systems for drones, robots, and autonomous machines.
Our work is in service of our customers who work factory floors, tend farmland, move medicine, and defend our country.
Atlas is built to supply them quickly, and supply them in volume. Their progress should no longer be held back by a motor or actuator.
Christian Mochen retweeted
It’s genuinely impressive to see a hardware company invest this seriously in their website. @atlasmotion is one of the strongest I’ve seen from the industry in a long time.
Christian Mochen retweeted
successful robotic automation for different problems will need different actuators/reducers and to find the right answer it requires connecting the intent -> design -> manufacturing data -> performance data and back 1/
I've spent years in China building robots. I can tell you this playbook won't work:
Pre-seed $: Invent the ultimate actuator.
Seed $$: Figure out mass production.
The hard part isn't inventing a beautiful topology. It's process engineering.
The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required.
This isn't something you can solve by buying traditional automation lines either. It requires:
1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process.
2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment.
3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing.
4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments.
5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter.
The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes.
Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else.
I don't think most people in SF clock this.
Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials?
To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years?
Do you have the skill set and management systems in place to do this?
That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better.
Automate simulation and design optimization.
Automate assembly and physical testing.
Automate the design and execution of process experiments.
Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy.
The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves.
Then the same robotic line mass-manufactures the product.
PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video).
I hope people understand this reality. I'll link more examples in the replies.
People are starting to realize.
This is why we built Atlas the way we did.
I've spent years in China building robots. I can tell you this playbook won't work:
Pre-seed $: Invent the ultimate actuator.
Seed $$: Figure out mass production.
The hard part isn't inventing a beautiful topology. It's process engineering.
The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required.
This isn't something you can solve by buying traditional automation lines either. It requires:
1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process.
2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment.
3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing.
4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments.
5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter.
The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes.
Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else.
I don't think most people in SF clock this.
Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials?
To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years?
Do you have the skill set and management systems in place to do this?
That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better.
Automate simulation and design optimization.
Automate assembly and physical testing.
Automate the design and execution of process experiments.
Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy.
The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves.
Then the same robotic line mass-manufactures the product.
PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video).
I hope people understand this reality. I'll link more examples in the replies.
If you believe robotics can maximize human potential, have exceptional ability in an area you love, have a passion for solving problems, and want to have fun—send me a dm.
I promise we will chat. Carpe diem!
Christian Mochen retweeted
Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are:
• Proprietary data
• Network effects
• A physical component
Christian Mochen retweeted
Extremely excited for these guys @atlasmotion - western indsutrial supply chains are constrained and not getting better overnight.
@CMochen @tombaron @carlodlrs are setting out to change just that - starting with a facility in Long Beach and bridging the Pacific divide with joint manufacturing 🇺🇸🇵🇭
great hat @nunzi46
30 days of Hard Tech: Day 3 🚀
Varda announced their $250m Series D
I break it down with Mike Annunziata (Mike is your favorite VC's favorite VC)
He was the first check in Varda and now a board member
"At its core, Varda is a company doing in-space manufacturing and reentry and that capability unlocks very cool business models across defense, pharmaceuticals, and semiconductors"
"We'll be generating significant revenue off of those missions and it's not all R&D. Every mission we launch, we learn. All that we learn goes into the next vehicle and into the next series of vehicles"
"We will know we've been successful when reentry is as boring as launch"
Christian Mochen retweeted
I watched the state of the force, then I had the kids watch it. The three most important words: Autonomous War Command.
Christian Mochen retweeted
Panasonic, Dyson, Nidec, Toshiba, Mitsubishi and Toyota all manufacture in the Philippines. An ecosystem producing motors, components and finished products by the millions.
Neil and EJ are two of the engineers building Atlas who came out of that world.
Neil came to Atlas from Dyson, where he led electromagnetic design for the motor inside the famous Airwrap. He obsesses over the unglamorous details in winding, overmoulding, busbar fusing, that turn a great motor design into something you can actually make thousands of times.
EJ started out building ultra-efficient vehicles for the Shell Eco-marathon at UST. He later joined Dyson as a mechatronics engineer, working across electromechanical design, process engineering, and line automation.
When we decided to start by building in the Philippines, the bet was that world-class engineering and manufacturing talent was already there, with years of experience building really difficult products.
We feel very lucky to have them.
Christian Mochen retweeted
Introducing the Atlas Standard Motor Series
Our head of engineering @AveryMcmillan15 on what we chose to build and why:
spot on once again
i hear (frequently) from people who try to over-intellectualize reindustrialization through various lenses in finance, economics, labor, technology, "free" market competition, etc. it's quite simple and doesn't require hardly any justification at this point. let me paint you a picture: the final crescendo of super intelligence is total mastery of atoms, and a semi-likely multipolar world of superpowers. the intermediate step that's missing right now is to build as much net-new tech-enabled capacity as possible. the transfer of tribal and process knowledge into compute is critical and very similar to how we've learned to control self-driving cars and robots. this is not something even the smartest model can predict/simulate with the level of necessary precision for real production, and these are glaring open deficiencies in all frontier models until they have data (live factories) to feast on. that learning rate will increase vastly as a function of overall tech-enabled capacity, and potentially displace frontier labs because they lack production experience and actual capacity themselves once intelligence is commoditized. the faster we do all of this the faster we create the potential for actual abundance.
Christian Mochen retweeted
After 9 years. 16K+ family homes. 500M sqft inside real homes covered, we know home robots are being built in wrong order.
So we built Matic the way nature grows a child.
Finally, sharing our vision:
1. Why robots get stuck at the demo
2. Why labs can't ship robots
3. Why we started with eyes, not hands
4. The 99.999% rule in robotics
5. Where Matic is going next
1. Why most robots get stuck at demos
Robot demos look brilliant in a room arranged around its limitations: familiar lighting, predictable furniture, nothing troublesome on the floor.
A family’s home makes no such accommodations. There are cables, toys, shifting rugs and, occasionally, dog poop. Yes, humanoids will need to avoid that too.
Repeating a task under familiar conditions of uncluttered floors is not the same as handling an unfamiliar home with all its chaos.
2. Why labs can't ship robots
SLAM (Simultaneous Localization and Mapping) is how a robot maps its surroundings and locates itself within them. Ask most academics and they'll tell you it's a solved problem.
But real homes are the most chaotic spaces that exist.
There are glass doors that look like open doorways, mirrors, stairs, rugs, charging cables, and Legos placed all around.
So ask yourself: if indoor mapping and navigation is "solved," where are the robots? Why aren't airports, hotels and grocery stores full of them?
Calling it “solved” misses the question families actually care about: can I leave this thing alone and trust it?
3. Why we started with eyes, not hands
Nature doesn't give birth to a fully working human. We took our cue from how nature develops humans: capabilities built on one another.
Children learn to see before they learn to grab, and to grab before they learn to plan and handle the unexpected.
Perception comes first. Manipulation follows. More complex responsibilities come after that. At every stage, the robot must earn the next job by doing its current one well enough.
Before asking a robot to pick up a sock, we wanted it to understand where it was, where the sock was, and how to reach it.
Most of the industry started at the top, with humanoids. We started at the bottom, with a robot whose job is to see and move through a home precisely with 1cm accuracy in any lighting condition.
Floor cleaning gave that foundation an immediately useful job. It forced us to confront navigation, clutter, privacy and everyday reliability before reaching for more complex chores.
The floor cleaner isn’t a detour from our larger ambition. It is how we’re building toward it.
4. The 99.999% rule in robotics
At 90%, one in ten decisions is wrong, and a robot makes thousands in a single clean. That's the robot that bumps, gets lost, and falls down the stairs.
Every extra nine is a new mountain. The failures get rarer, harder to find, and exponentially harder to fix. They barely even happen until they happen in your home.
Matic's visual SLAM runs at 99.999% in 16K+ homes. We’re the ONLY unsupervised home robot at scale with pure vision-only full autonomy. If you ask us, the gap between 80% and 99.999% is where our 9 years of engineering went.
We close that gap inside real homes. When Matic handles something imperfectly, it saves a short clip and keeps it on the device. It only leaves if the family chooses to share it. If they do, it gets labeled, fed back into the model, and every Matic gets smarter, including theirs.
Families control what leaves their homes. We do the work of making the product better.
Better robots earn trust > trust brings more homes > and more homes teach the robot more.
5. Where Matic is going next
Phase one was perception and it's nearly done. Matic has covered 500 million square feet, 400K miles in thousands of real lived-in homes.
Now phase two is going to be about manipulation. And our idea is to build a robot that doesn't just move through your home, but acts in it to eliminate even more chores.
Each step must be useful today, not justified by something we promise tomorrow. The evolution of Matic is the revolution.
The goal isn’t to put the most impressive humanoid in your living room.
It’s giving your family time and energy back through robots that earn your trust, protect your privacy, and help without becoming another responsibility.
More time for each other. Less work getting in the way.
That’s Matic.
Get yours today at maticrobots.com
Christian Mochen retweeted
We’ve added a few stops: Boston, Austin, Dallas, Raleigh-Durham, Tallinn, Riga, Istanbul, Athens, and London.
Who else should we come see?
Best engineering team in the world.
Great time speaking with Danny at the Strativ group kicking off his new podcast!
Take a listen, full podcast linked in the comments!
@atlasmotion
Christian Mochen retweeted
Simulation tools without tested designs that it produced to anchor it are just pretty picture making tools.
Estimate the Limit.
Test beyond the Limit.
Know the limit.
@atlasmotion