[mikepurvis]

Your conclusions are true, though I think it may be helpful to further subdivide into some categories as far as what the target market was and where they were at with technical readiness.

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)

[ksec]

>They were never fast enough or reliable enough to be competitive with the minimum wage labour that they would have displaced

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.

[ethbr0]

More specifically, humans have an incredible high adaptability:cost ratio.

There aren't many businesses where precision:cost or volume:time are more important than labor costs.

[iamstupidsimple]

That may be true today, but it might not last forever. Labor cost in 1st world nations is skyrocketing (due to cost of living mainly) compared to poorer nations, and there may come a time when robotics becomes relatively competitive. Especially when those cheap labor countries start having the same effect.

[mikepurvis]

I think a lot of that comes in the form of partial and adaptive automation, though— like self-checkout at the grocery store, where it's "automation", but only in the sense that the self-checkout console enabled outsourcing the pick and place part of the work onto the consumer.

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.

[ethbr0]

I've worked in software automation for about a decade now, and that's been my learned wisdom too.

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.

[ksec]

Well, while cost are high in first world country, labours are mostly limited to services sector.

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.

[mikepurvis]

> program a new task which requires software testing and engineers

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/

[yunohn]

> Especially when those cheap labor countries start having the same effect.

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.

[soheil]

Starsky value prop was teleop, but that was the same thing that cooled investors. Adding an extra 20-100ms latency to driving is akin to driving after two drinks. Operating a vehicle 10x larger than the ones on the road does not make this problem smaller.

Operating large trucks is not a game VCs wanted to play.

[mikepurvis]

I don't think it was ever meant to be live driving at highway speeds:

https://www.forbes.com/sites/stefanseltz-axmacher/2020/06/16...

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).

[soheil]

I just don't think you can call that a real value prop if it's only for when the truck is stuck or a few minor edge cases. There are many scenarios where self-driving may not work or behave erratically so if their version of teleop doesn't solve those then not sure how Starsky argued they were ahead of competition.

Additionally I think investors backed out primarily because of risks associated with operating an autonomous fleet, not the shortcomings of the tech itself.

[mikepurvis]

I feel that it covers an awful lot of them. If you cap teleop driving at 20km/h or something (or maybe a dynamic cap based on your rtt), that still covers all of the parking lot scenarios, as well many sensor-failure situations, like if you needed to crawl along in the right hand lane because it's a blizzard and the radar is blind.

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?

[soheil]

> We also saw that investors really didn’t like the business model of being the operator, and that our heavy investment into safety didn’t translate for investors.

This is what Stefan said here [0]. 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?

[0] https://medium.com/starsky-robotics-blog/the-end-of-starsky-...