Huanbin Chen

[Yeok @yeokch]

I’ve spent years deploying robotics - ‘gear backlash’ is a fundamental problem with actuators. Backlash is among the main reasons why precision cannot be achieved.

This toy from @Daniel_Van_Zant illustrates backlash brilliantly!

You can see that there is a 'dead zone' where the motion doesn’t get transferred from the back wheel to the front wheel. Even though gears appear to mesh, because the gear profile / shaft isn’t perfect, the back wheel wobbles a bit without actually moving the gears.

In actuators, this blacklash translates into a situation where the rotor turns, but the output shaft doesn’t. In a robot, this means:

1.⁠ ⁠Your robot joint may be at a different position compared to where the encoder thinks it is

2.⁠ ⁠Your robot joint will wobble between two states (like the back wheel of the toy car) even though the rotor is static. You can feel this by playing with robot joints like you’re giving it robot physiotherapy.

The problem with gear backlash in actuators is that it is microscopic. This is the main reason why even the best arms say ‘0.1mm accuracy’ because these dead zones add up sequentially across joints, resulting in a stacked backlash.

Backlash also gets worse as gearboxes wear out. QDDs with their planetary gearboxes wear out fastest because stress is focused on a few teeth compared to harmonic actuators, which is a trade-off for other benefits.

Improvement in the near future is essentially better gearbox topologies, better simulation-driven gear profiles, searching for suitable materials, more precise machining, better modeling of wear and tear over time (so you can work around it), etc

I truly believe that the optimum actuator is a long way ahead. It is like chips in the early 2000s, which went from 20nm to 10nm to now 2nm. We will iterate to the best actuator over time and the team who can iterate the fastest will win.

[Ryan Chan @Ryan_Resolution]

10 minutes Metal Arm Station deployment setup

Collab with Caldera with @yeokch @raghav2956 @HuanbinChe0dde.

Ready for zero-shot rollouts with Dream-1 @dreamlabs_ai

[Yeok @yeokch]

We're not going to keep pace with China's actuator development by doing process engineering with humans.

China isn't going to reach actuators with precision that can perform surgery on cells with their current playbook of doing process engineering with humans.

I believe everyone will move towards automated design and process improvement - the question is just how.

Let me give you a real world example of how this works first

I can design our Caldera-0-3115 motors to have an airgap of 0.08mm airgap instead of 0.16mm airgap but I can't manufacture it. If i could, this would translate to about 8% max torque at 2.5kW max throttle - which Counter UAS folks would absolutely love. This is more impactful than any design improvement I can make from optimizing EM design with Ansys and maaaybe set a new world record.

But I just can't manufacture it - even if I did, it would die within x hours of flight time because at some point the stator and magnets would start colliding.

Because my current manufacturing process creates error tolerance stackup from

1. flipping/refixturing of the multi-step lathing process of the rotor bell not perfectly coaxial
2. press fits
3. adhesives
4. tolerancing the grinding of magnets
5. at 22k rpm, the remaining 0.x miligrams of imbalances after dynamic balancing that sway the bell from one side to another as well which requires some margin.
6. warping from repeated thermal expansion

I can't improve them without CNCing with different tools / cutting operations / fixtures; try different parameters for press fit, try different adhesives which responds differently to thermal expansion, etc.

And that's just the airgap. Every feature has its own quirks and optimizations to be made.

And this isn't a one time thing, this is a marathon that all actuator-makers are running, always, for every SKU. With the added complexity of a gearbox.

So I understand

Why @atlasmotion does custom motor design + US prototyping then does volume production in the Philippines. Their experienced manufacturing labor gives you the flexibility to handle different products AND process experimentation.

Why @parthingle_x is building custom CNCs specialized for motors/actuators and a factory knowledge base with, telemetry and scheduling capabilities. This makes sense to me because it's a specialized tool to automatedly improve to cut better parts for motors.

Why Almetra capture live factory floor video, immediately convert raw human movement into structured digital work steps, and orchestrate real-time floor feedback that systematically drives automated process improvements.

We at Caldera are going the route of fully automating design and process iteration via factory intelligence, robotic assembly and software integration over all machines that participate in actuator manufacturing.

All of us are building pieces of the same capability: running more experiments, learning from them faster, and getting the improvements into production. That's what US manufacturing needs to compete. The motor you can build today is the starting point. What matters is how quickly you can build a better one.

Please tag people with a blurb of what they're doing if you think they are working in this field as well! It'd be super interesting to see what everyone is building and what are their takes.

[Yeok @yeokch]

Told @JasonMa2020 @DynaRobotics VLAs bad for manufacturing because precision doesn't scale with high quality data, it just teaches it to be more generalizable. and he said

'im not saying its a skill issue but...'

kill me please

He's right - he's saying turn precision problem into a retry and recovery problem that VLAs are good at. Humans cannot one-shot the same task either. We feel around with touch and vision until it works. Like plugging in a USB or inserting a shaft. Very light pressure around where things should go, to give enough signal to tactile fingers @wuji_global / visual deformation of grippers @GeneralistAI / joint torque @UnitreeRobotics

So obvious in hindsight. Time to re-collect trajectories for training @HuanbinChe0dde

[Yeok @yeokch]

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.