Edit: I'm not alone, https://style.mla.org/dont-bury-the-lede/
Growing up in a strict Lutheran household in the southwest England town of Flenkelshire, Elias Nathaniel "Kazoo" Pendleton III did not immediately stand out among his peers. Born with dull red hair, one leg three inches shorter than the other, and shoulders that somehow resembled cornish hens, young Elias was a frequent target for the town bullies. A child at that time has only three options: fight harder, run faster, or invent some kind of device that would enable him to escape his tormentors. Luckily (by chance or by fate), Flenkelshire was home to a radio-electronics store, Bundleron's Radio and Horseshoe Supplies, which gave young Elias just the right ingredients to hatch his escape plan. And hatch a plan he did, though it would take twenty years for the town to understand exactly what had happened.
The first trap was set in the Fall of 1951. Winston Churchill had returned to power. The Festival of Britain had just wrapped up and lit the imagination of attendees and non-attendees alike. And Elias Nathaniel "Kazoo" Pendleton III, now well-armed with a stock of electronics, metalwork, and several years of intense study, went into action...
One time I inadvertently hit print on a recipe like this and the print dialog estimated 45 pages.
That is where it’s at.
Based on this reasoning, the United States Copyright Office Compendium, the Office’s manual for examiners, states that a mere listing of ingredients or contents is not copyrightable, as lists are not protected by copyright law (chapter 314.4(F)). The Office has also stated that a “simple set of directions” is uncopyrightable.
In addition, courts have found that recipes are wholly factual and functional, and therefore uncopyrightable. As the Sixth Circuit described in Tomaydo-Tomahdo, LLC v. Vozary, “the list of ingredients is merely a factual statement, and as previously discussed, facts are not copyrightable. Furthermore, a recipe’s instructions, as functional directions, are statutorily excluded from copyright protection.”
They're ostensibly informational, but 99% of people consuming them aren't genuinely looking to cook the thing, travel to the place, or buy and renovate a house.
Probably could build it with regex, actually.
> Before Y was murdered, they lived in X. X is a quiet town, the type of place where you don't need to lock your doors. Y has a happy upbringing collecting flowers along the river at...
Like we get it, this is the first half of every 1-hour long true-crime podcast. Also quite often the first half of every long-form article.
I do not mind 5 minutes of backstory on a 60 minute podcast. I do mind 2 minutes of backstory on a 5 minute read.
I'm like, "you weren't there man!!!"
PS: I'm not the only one who thinks wondery shows are "overproduced"
How every NPR story about criminal justice starts.
> Intrinsic is working to unlock the creative and economic potential of industrial robotics
...is under the fold, under two paragraphs and an image
The lede (“leed”) is the most important statement in the story. The word comes from “lead” (also pronounced “leed”), because it's the statement that everything else should follow. It's conventionally spelt differently to avoid confusion with “leading”.
The whole point of a blog is to be a personal log.
I’m in manufacturing. Machinery is highly specialised. Making a generic robot without taking up huge amount of floor space and/or huge leaps in programming is like… Kubernetes being good for hosting your moms book club blog.
In general you’re not wrong. It’s also an easy prediction to make because almost all new robotics efforts fail.
I’ll just say that they’ve been working with manufacturers for years on test projects. So this isn’t really a new thing so much as a formalization of whatever they came up with.
I’m still not sure how this stuff will go (X is of course for experimental stuff) but if the project has “graduated” to a company than I’d presume they have some revenue plan and potential customers. I could imagine that at least one of their trial projects with a manufacturer has worked out, and they think they can find more partners for this work. (I only scanned the beginning of the article)
They have been experimenting and developing a LOT of stuff since 2013 and plenty of it sadly does just get completely sacked and mothballed (and I have my personal gripes with the great projects they could have open sourced but instead they just let the bits rot). That said I think with how much I saw them working on they probably could find applications where profit is a real possibility.
I would LOVE to be proven wrong, but Google's track record with bringing products to market, and actually keeping them for longer than a media boost, is downright depressing.
At some point it feels like every Google side project is just a media buy for their real business: hiring engineers to drive more ad revenue, engineers who are enticed by shiny projects like these.
The leap from an AI model learning how to replicate a behaviour (e.g. evolving walking to solve problems https://unitylist.com/p/2id/walking-ai ) to reasoning about it in terms of actuators and physical feedback, to assembling a physical model out of a relatively small list of parts seems like a solvable engineering problem when it is broken out into a pipeline.
Those robot parts are basically a version of mechano with actuators that a model would map a behavior to, and the robots in the article would assemble them. When you look at something like Lego or Mechano as an intermediate representation to construct buildings out of, where all objects made from it are essentially a directed graph of those elements, robots designing and building robots seems like less than 20 years away.
e.g. we could functionally specify to an ML model, "produce a digraph of these element parts that has these degrees of freedom, and then load or derive a model that solves for this outcome within the domain of those degrees, where outcome is 'plug cables into a board' "
This is not manual or bespoke and it has sensors. The videos are incredible and they work in real life already.
This one is it moving petri dishes full of liquid without spilling! This is obviously not being pre-programmed to move along some kind of 1980s style fixed paths for welding parts as Alphabet apparently thinks everyone is still doing. The obliviousness of suggesting that using ML models for robotic control is some unique new idea is really off-putting. Mujin has been around since 2012.
The more the merrier, of course, but just dismissing the state of the industry and claiming you've made a huge technology leap (compared to the 80s and 90s instead of something harder)... ugh.
Intrinsic/Alphabet are not suggesting they are somehow unaware of easily-Google-able state of the art in ML robotics. They literally used to own Boston Dynamics.
From the post, the second demo of their tech (“Two robots use perception, force control, and multi-robot planning to assemble a simple piece of furniture”), is very clearly much more than “moving Petri dishes”.
FAANG has access to the leading factories in Shenzhen, and heavily utilize robot tech in their HW supply chains.
Do you know what the N stands for in FAANG?
Facebook doesn’t have better Shenzhen access than Microsoft or you know, hardware focused companies like nvidia.
Using “FAANG” is a red flag that the commenter has no idea wtf they are talking about when it comes to the hardware industry.
A pick and place is 2-axis movement with a suction cup. This is controlling a robot arm with a ton of degrees of freedom and developing paths for moving through all those degrees of freedom without hitting anything and using internal models to do so.
I suppose in some very broad sense it looks similar, but the difficulty of x-y + down is way, way lower than what you're seeing in that video.
Look at cars for example. Tell an ML model to "make a car that can drive over rocks" and it will give you a rock crawler with the motor in a location where it won't be easy to fix. Tell the ML model to "make a car that is easy to fix" and it will make a car that is probably unreliable. Tell it to make a car that is reliable and easy to fix you will get a car with no motor at all.
I'm not saying it's impossible, because it obviously is possible. I just think your 20 year time-frame is hopelessly optimistic. What good is an ML model that takes 10 weeks to setup that solves a problem that only takes 2 weeks to solve without ML?
Here's much the same job, being done almost 50 years ago, by a robot at the Stanford AI lab. This robot has both vision and force feedback, and uses them to assemble an automotive water pump. It does the coarse alignment visually, and the fine alignment by feel.
The correct feeling here though should be compassion, here is a group that has been safely nestled in the arms of Google X and is now being pushed out of the nest like so many projects before it, which currently has one such company, Waymo, that is currently not yet dead. Statistically speaking, it is unlikely they will be able to pupate into a products company before they run out of time.
That said, it is also a truism that the constraints on robotics 50 years ago are not the constraints on robotics today. Re-implementing those ideas which had merit before but lacked a sufficiently robust ecosystem to be practical might in fact be really useful today. One hopes that they have the perspective of the excellent technical reports that SAIL produced to guide their development.
Another possible difference -- how much programming time did it take to teach the Stanford robot to assemble the water pump? Sounds like Intrinsic trained the robots to do this with little supervision.
It seems to me that this might represent pretty solid progress, although not exponential/paradigm-shift scale like we've seen in some other industries in that period, and nothing in the Intrinsic videos seemed like it was above par for other automation companies I've seen recently. But since you seem to be in the industry, what's your take on whether they seem to be ahead of the game, or even just realistic, with claims like:
> In one instance, we trained a robot in two hours to complete a USB connection task that would take hundreds of hours to program. In other tests, we orchestrated multiple robot arms to assemble an architectural installation and a simple piece of furniture. None of this is realistic or affordable to automate today — and there are millions of other examples like this in businesses around the world.
Here's a longer version in a larger size, either not sped up or not sped up so much. It's using a simple strategy of approaching the socket at an slightly off angle and then twisting into alignment.
That's a standard strategy. Compare this video of assembling Lego blocks with an industrial robot. Note the little twist moves.
Did the machine learning come up with that, or was it preprogrammed? Did ML re-invent remote center compliance? That would be progress.
Rod Brooks went down this road with Rethink Robotics. They went bust.
You can certainly do what they're showing. It's making a profit on it that's hard.
There are more complex ones but I can’t find the links.
Bonus: the ROI changes as you invest in either bucket.
Apple was once into design for assembly. The Macintosh IIci was Apple's peak at design for assembly. It was designed for vertical assembly. Everything clicks into place with a straight-down insertion move. No wiring harnesses. The power supply plugs into the motherboard. An automated plant in Fremont CA did the assembly.
Then Apple gave up on design for assembly and went to offshoring and cheap labor.
Motorola flip phones were designed for automated assembly. All parts were on boards, and the boards were stacked and compressed into a solid block, with bumps on the boards making connections to the next layer. A tough, reliable phone resulted.
Then Motorola gave up and went to offshoring and cheap labor.
Sony pioneered this approach. The Sony Walkman,, the original tape unit with motors and contra-rotating flywheels, was built for vertical assembly and assembled by a simple Cartesian robot.
Then came the iPod.
When it comes to MBA enclosure it's likely the infill or separation problems with long thin walls that led them to abandon injection molding. Even then it's not done on a mill in one take. Things like speaker grilles are probably something like EDM or etching rather than 8000 operations with 0.2mm drill bit.
And as far as parlor ticks go this is not particularly impressive, compared to old ones like turning cube inside a cube on a lathe.
Tools like UNISURF would probably not be able to handle the extreme level of detail on most of that model. The very long tool lengths you see in the video are much more complex than they look and require effort both from software and hardware to prevent chatter and breakage on titanium. The clearances in the video are also extremely tight in places, and while you could have guess-and-checked that in the 70s, it’s a very different workflow than the simulators that are basically standard use today.
CNC machining is a multi-billion dollar industry populated by smart people. While the fundamental technology of “spinning cutter driven by computer controlled motors” hasn’t changed in 50 years, the R&D departments aren’t asleep at the wheel.
From the initial endmill size they use and the generous machine sizes (which can be extrapolated from chuck/table size) we can easily tell rigidity/chatter wasn't a particular challenge here. And the finishing passes are so delicate they don't even bother with coolant for that.
Yes the code to run all this would be a monumental undertaking in the 70s but it is done once and can be still entirely cost efficient as far as mass production is concerned. The thing new machines have is the running speed (for same precision work). These did improve a lot and make certain classes of products economically viable.
I sometimes get it. Then when I hear robots were used, I wonder if it's really necassary to always go to the cheapest labor route.
For years, I held it against Apple for moving manufacturing, but gave up when everyone followed.
There's another axis here, which is how our desire for a product overlaps with DFM. It could be the case that offshoring to cheap labor actually increased the manufacturing costs 2x, but enabled a product that would sell 10x better than its DFM counterpart.
(I have no data to say that is the case, only the intution that these things are complicated systems which rarely come down to single-issue decisions.)
on edit: I also went ahead and made that article a seperate post here:
Currently, you can either use fixtures and jigs and specialized machines and run fast, use humans and run medium speed, or use AI and generic robotics and run REALLY SLOWLY.
Where's the value prop?
* Rethink Robotics https://www.zdnet.com/article/sudden-unexpected-demise-of-re...
* Anki https://spectrum.ieee.org/automaton/robotics/home-robots/con...
* Jibo https://spectrum.ieee.org/automaton/robotics/home-robots/jib...
* Blue Workforce https://www.therobotreport.com/blue-workforce-robot-files-ba...
* Mayfield Robotics (Kuri) https://www.heykuri.com/blog/important_difficult_announcemen...
* Starsky Robotics https://www.bizjournals.com/sanfrancisco/news/2020/03/20/why...
* Reach Robotics https://www.therobotreport.com/reach-robotics-shuts-down-con...
* Google Schaft https://www.theverge.com/2018/11/15/18096469/google-robotics...
* Willow Garage https://www.bloomberg.com/news/articles/2014-02-20/robotics-...
* Honda Asimo https://www.theverge.com/2018/6/28/17514134/honda-asimo-huma...
* Amazon Vesta https://venturebeat.com/2019/09/28/amazons-vesta-no-show-hig...
Everyone thinks that they are somehow different, but all these firms fail for the same reason. Robotics is hard. The market is not that big. Lots of costs. Investors are skittish. The combination of those things isn't that good.
Like, some of them (Anki, Jibo, Mayfield, Asimo, Reach) were 100% toys, and were always going to be at the extreme end price-wise trying to compete with increasingly "smart" toys being manufactured by regular toy companies with regular toy company processes, volumes, and margins.
Others (Rethink, Willow, Schaft, Blue) were trying to do something really ambitious and potentially provide B2B value, but were never far enough along to have a compelling value proposition for the end users they were targeting. They were never fast enough or reliable enough to be competitive with the minimum wage labour that they would have displaced— if robots are hard, then mobile robots are harder, and mobile manipulators are the hardest of all.
I think the saddest story in here is still Starsky, because they weren't in either of these groups: they really did have a clear value proposition, and they were technically there as far as delivering on it. The market needs what they were offering; they seemingly just ran out of runway at a time when investors were too starry-eyed about vaporous promises of L4 autonomy to want to back a company working on a viable hybrid solution.
(Disclosure: I work for a B2B mobile robotics company)
This probably sums up well. Human are extremely adaptable. To point if we are measured as 100 then no Robot is even 1.
There is a whole reason why even Foxconn gave up using Foxconn Robot, some task are just insanely easier and cheaper for a human to do it. They’re not easily automatable and even if we could the cost benefits doesn't make any sense.
So instead of having human plugging in DIMM RAM or M.2 SSD, now they are all soldered on the logic board using machines with automation.
There aren't many businesses where precision:cost or volume:time are more important than labor costs.
Or elsewhere in the thread, the example of moving a previously-modular computer part onto the logic board, so that it can be soldered on rather than needing to be installed later in the assembly process.
Companies like Rethink weren't in this world— they were trying to build a manipulator (Baxter) which was a drop-in replacement for a person doing pick and place work. Which has a certain appeal, if it works ("no need to retool anything; just buy it and put it to work!"), but it puts you up against the direct price comparison of just having a human continue to do that job.
Don't try and boil the ocean: see what COTS is available, adapt your process to be able to leverage that, plug it in, and move on to the next project
As commentor above noted, volumes have to approach obscene to justify a moderate+ amount of custom, one-off implementation work.
In manufacturing most of these labour are still in Asia. And the cost / productivity is still insanely cheap. It isn't just the cost of the Robot itself, but to program a new task which requires software testing and engineers. So the cost barrier is still so far apart. Foxconn make hundreds of millions of smartphone every year. You would have thought saving $10 per phone would have net them a few billions extra profits. And yet their employment rate has remained largely the same.
If and If, US and Tech managed to do this ( there is nothing even remotely close in the next 10 years, but let say somehow there is for the sake of argument ), this will be the largest reset of manufacturing and likely be Industrial Revolution 3.0.
To be fair, Rethink did understand this part, and part of their pitch was that it was supposed to be easy to teach their robots tasks with a kind of observe/repeat flow; here's a video from way back in 2012 showing where they were trying to go with this: https://www.youtube.com/watch?v=gXOkWuSCkRI
They're not the only ones either, UR has also placed a heavy emphasis on safety and ease of task training, though unlike Rethink, I don't believe their systems come with any built-in sensing, so it really is limited to just mindlessly repeating exactly what you show it: https://www.universal-robots.com/academy/
You missed the comment’s pivotal point. As developing countries, well, develop, higher labor prices will affect the entire supply chain. It’s a Good Thing (TM), and that’s why we’ll need better robotics in that future.
Operating large trucks is not a game VCs wanted to play.
The point was that it was an autonomous system that could ask for help, and the "help" scenarios would mostly be cases where the truck was already stopped or at very low speeds: navigating a construction zone, a transfer yard, etc. Possibly in some of these situations it wasn't even wheel-to-wheel, but rather a system of choosing between a handful of high-level courses of action for the machine to then proceed with, or helping the perception system classify an unknown object it was looking at.
I didn't sense from the postmortem articles by Stefan that safety concerns were what killed it. It was investors being disappointed that they weren't trying to build a truck without a steering wheel at all, since that was clearly where Uber, Waymo, Tesla, and others were headed (and at least at the time, external safety concerns were not seemingly impacting any of them).
Additionally I think investors backed out primarily because of risks associated with operating an autonomous fleet, not the shortcomings of the tech itself.
In any case, the Forbes article specifically addresses how they modeled these things:
"Up ahead a deer jumps into the truck’s lane and hundreds of miles away a teleoperator is asked to take control of the vehicle. But they aren’t able to in time – either the deer jumped too quickly or the teleoperator wasn’t able to get situationally aware or worse yet: the cellular connectivity isn’t good enough!
Such was the situation painted to me time after time after time as CEO of Starsky Robotics, whose remote-assisted autonomous trucks were supposed to face exactly such a scenario. And yet, it was an entirely false scenario.
As I’ve written about before, safety doesn’t mean that everything always works perfectly, in fact it’s quite the opposite. To make a system safe is to intimately understand where, when, and how it will break and making sure that those failures are acceptable."
The fleet argument also confuses me; hasn't that been the Waymo/Uber pitch since forever, a centrally owned and managed fleet of autonomous vehicles for hire? Why would that be considered an especially risky direction?
This is what Stefan said here . Honestly I hear contradicting reasons for the failure. It could be that their investors had a different risk tolerance than Waymo/Uber's.
I guess I'm confused, sure, teleop could cover a lot of the edge cases but if there is a fat long tail you still end up with a pretty unsafe technology. The deer example is kind of a distraction and goes to show that maybe Starsky had a problem imagining and classifying catastrophic failure events. For every deer jumping in front of the vehicle there is a 10x more serious scenario that could lead to human fatalities.
After reading his posts I'm still confused about the reasons they failed. Can you list the reasons from high priority to low as to why they failed?
 As opposed to robots that, say, fight wars. But we call those things "missiles" and "fighter jets" and "drones" not robots.
> you don't really need to worry much about supporting legacy products years after selling them to customers
You really, really do. Missiles are expensive, and stay in inventories for a very long time, and they need to be made compatible with every update to every platform that can make use of them. That wouldn't be so bad, but then you also need to prove that they work with all those platforms. This is hard.
> they are not expected to be functioning after just one use.
Missiles are only fired once, but that doesn't mean they are used once. The typical "use" of an aircraft carried missile is that it is attached to a plane, powered up, and then the plane does a sortie and lands, and then the missile is removed and maintained. There is a lot of maintenance that is done to the missile daily.
Likely what is meant is the market for current state of the art robotics, which have limitations and are cost prohibitive (capital wise).
 https://en.wikipedia.org/wiki/History_of_numerical_contr ol
This is truly a crossing the chasm problem.
If your robot can't receive either of those labels, your robot company is doomed to a slow death.
https://www.youtube.com/watch?v=TUx-ljgB-5Q shows some footage of the robotics they use.
There were hundreds of copycats startups in China following the trendy business ideas at the moments.
The Groupon era
It just looks like not enough money in robotics, not that robotics are wasting them
Maybe with a "software" approach to this we'll see better and more open tools.
This looks like something designed to attract ignorant investors/talent who think small time manufacturing looks like a Ford plant but with less robots and more humans. In reality it looks something closer to Grandma's kitchen on Thanksgiving. How are you gonna stick a robot in there and have Uncle Fred program it?
I can't see this as anything other than a flashy high school engineering project. Much wow! little application.
Source: Work in domestic manufacturing. <$50 million company. Mostly do government/military electronics building.
This is definitely far from mass adoption. But somewhere certain expensive product might benefit from this. Guess: mechanical watch assembly, given the amount of manual labor, and the claimed learning ability, it seems possible for a robot to assemble a 1milions worth of Swiss watch.
The latter has a much broader customer base. From picking fruits to folding shirts to installing a headliner into a new vehicle, there are many applications.
The ability to plug in cables and whatnot looks like a useful ability but I'm guessing this will just be sort of like really good traditional robotic control software rather than anything really fundamentally different.
Anecdotally, I've heard that FANUCs don't respond well at all to any input deviation.
This is a piece of the puzzle of building a machine of machines that can make almost anything without human intervention.
Are they hiring interns?
a self replicating robot / factory / 3d-printer, a potentially new form of life
So, two answers - 1. folding laundry is a difficult technical challenge. 2. when we get a robot to do that task, we won't call it a robot.
Some people do refer to automatic vacuums and other things that move automatically robots too.
Folding laundry (and more generally, manipulating deformable objects) is actually a pretty tough task for robots. So there has been some research on it, and you can find videos of robots doing it (very slowly). I guess we'll get there eventually, but right now even if it's possible, it's not at a level of speed, robustness and cost where it makes sense.
It's not good that this introductory post doesn't start right off with a problem to be solved. Instead it presents the credentials of the current leader.
If I had to pick out the problem, it would be this sentence, contained in the fourth paragraph:
> Currently just 10 countries manufacture 70% of the world’s goods.
In the fifth paragraph, we get a more clear phrasing of the problem:
> The surprisingly manual and bespoke process of teaching robots how to do things, which hasn’t changed much over the last few decades, is currently a cap on their potential to help more businesses.
Ok, so this is going to be a company that solves the problem of poor usability of industrial robots through machine learning. The larger goal is to put manufacturing capacity closer to consumers for better sustainability.
Is the implication here that they're aiming to automate away all of these jobs?
The implication of that in turn is that these US companies aren't willing to pay at a rate that would be competitive in the market
*There are exceptions, but they are rare.
Moonfruit, launched in 2000, was definitely not the first SaaS website builder. Geocities launched 6 years before it and there were dozens of them by the time Moonfruit came around.
While not a big lie, it's an odd way to start a post like this.
A main source of the original fortune that funded the creation of YC and thus Hacker News was the $49m sale to Yahoo! of Viaweb, a SaaS website builder (focused on ecommerce) founded by Paul Graham, Trevor Blackwell, and Robert Morris in 1995.
I suspect that Dr. Chelsea Finn's work in meta-learning (affiliated with Stanford and GBrain, when I saw it last year) might play a big part here, which is e.a. about generalisation of RL policies to out of domain tasks. (E.g. similar task, but slightly different tools, slightly different task, etc.)
Learning IRL (cameras and actuators) reinforcement learning policies is a huge time sink, so generalisation is a hugely important task. Related solutions can be found in simulation->real generalisation, also an active topic of research.
Hardware and mechanical is like 95% of the problem so there's a need to take the approach of making the machines that make and then add the software on top and developing synthetic task orientated data from that. E.g. the dishwasher, which works because its physically designed for washing plates and then automation was added. The robot arm is a general purpose technology that has been around in the same form since the 60s/70s. There are many options as alternatives (e.g. magnetic assembly or even self-assembly in certain industries) but ofc these are incredibly risky commercially.
I'm aware that this is just the first post and the above is well known in robotics development so excited to see what gets built!
There is a software authoring problem (which is where the ML bits are crucial).
If we had to program all robots like we had to with CNC machines, then programming them would be a high skill problem, even if we throw a lot of tools at it.
I can work my way through a Tormach, but is that really what I want to spend time with? The ultra low level specification of what I need done?
I'd love a pedal based training system with something like "Identify", "Orient", "Place", "Count", "Test" to teach it things in steps & get a program out of the demonstration (that donut computer vision project was amazing, because it showed you didn't really need ML to do these things).
Like we have people who are demonstration learners, I wish I could do something like that of going from many scenarios to a final one and have the robot to dissect every one of my actions into a flow-chart of its own.
My approach would be to manufacture custom arms for particular tasks and in principle 3d printing the arms is exactly what i'm getting at (e.g. that optimised physical design processes save on cost and improves performance much more than software + expensive externally manufactured arms). 3D printed arms with comparable repeat accuracy would be an excellent optimisation over buying v expensive Kuka products. Then you could start think about different mechanisms (compliant mech, soft parts etc) and control systems/software.
Kukas are not really just a couple of servos (e.g. encoders) and there are many examples from the 90s of self walking robots with little software too. There's good literature on "morphological computation" or Rolf Pfeifer's book How the Body Shapes the Way We Think: A New View of Intelligence.
Well, actually if they do some AI stuff that might be impressive.
I guess stationary robots are seen as less of a reputational risk in comparison with Boston Robotics nighmares.
There is an old saw about the transition from steam powered factories to electrical power. Initially the large steam engine was in one location, and basically its power was delivered by belts running off one central location. The factories initially tried to replace the steam engine with one big electric motor, and it worked ok but the factory was still a hub and spoke and pieces had to be moved from one spoke to the next.
It was not until a new generation of factories were built with many motors at any point in the factory that the modern line was built.
Of course this is a massive simplification, but I look at two robots using 10 m2 to assemble some Ikea cabinet, and think "awesome geekery" but if you want a factory producing pre-made furniture go back at least three-steps.
Robots that can replace a human arm in the assembly process just feel like we are replacing that big steam engine in the middle of the factory.
And, yes industrial robots is where you start, of course. But a factory can change its process to eliminate the need for a general purpose robot. But the home - that's a different story.
* Take up two "normal" sizes of a washing machine. A hopper accepts clothes, sorts them using RFID tags, and begins a run in a smaller drum, spins, dries and folds them. (yes, its probably magic but this would be on everyone's XMAS list)
* (completely foregoing everything I just said) a mobile robot arm that can learn where each item in a house belongs. 3D tracking, ML etc, and it picks up the toys my kids have left lying around.
* I am not sure where the "robot" vs "process" sits here, but food purchase and prep is a large time sink for many, but there seems to be a viable disintermediation of supermarkets - I mean if i choose a decent set of meals for a week, why send the food to the supermarket so it can use its shelves as a collection point to send it on to me. And if the food is picked so i get "nice meal on Saturday" plus "something with the extra Tues lunch"
I think there is a real possibility of robots making the middle class home like a B&B.
As Jerry Hall said, "My Mother told me if I wanted to keep a man I needed to be a Chef in the Kitchen, a Maid in the living room and a Whore in the bedroom. I said I would hire the first two and take care of the rest myself."
Edit: honestly I am not trying to be HN-negative, and I think all this investment is only going to build better robots. Which is a win. But I remain under-convinced that building general-purpose robots to replace general-purpose humans, when humans are already having the easy bits replaced by specific purpose robots is a good idea - it feels like running uphill.
The furniture assembly thing probably doesn't make sense for huge runs, but you could stick one in front of a modest warehouse and build 200 different products on demand.
On suoermarkets, yes Walmart and Tesco made a sensible decision to use their existing stores as feeders for delivery. But as the number of people taking food deliveries goes up, that starts being a commercial disadvantage - there are things you can do to improve a warehouse that you can't do if customers are walking around in it, there are car parks that aren't needed now, smaller stores in expensive locations. It's not like Walmart's going bust next week but the world is changing. Even Amazon will feel this - a fleet of international transit and drivers designed to drop one package off randomly is going to find that model under threat if I get a food delivery every three days - 99% of the time I will just have the other stuff i order come in the same box.
For every house, it makes sense that only one delivery company visits that house. If they are delivering every couple of days anyway just roll it in together. Place tour order and it will be with your regular Tuesday drop off - that's convenient mentally for most people, whereas "expect a knock on the door anytime in the next 36 hours" is less easy to manage - especially now we are not all locked indoors.
I think i have wondered off the point but it's always much simpler to change the process than to build a human-analogue robot.
We all have huge shifts in how we will live in the coming decades - to stave off climate change, to take advantage of what software can really offer. Making already efficient factories more efficient is the equivalent of looking for your keys under the lamppost- the big wins lie elsewhere.
Edit - the big five future changes
- urban planning (see Tokyo, or StrongTowns)
- Buikding energy efficiency (solar panels, heat exchangers)
- Transoortation (Food and people) & transportation (other)
But yeah scroll speed is ludicrous!