Yuli’s Law: On Domestic Utility Robots

The advancement of computer technology has allowed for many sci-tech miracles to occur in the past 70 years, and yet it still seems as if we’ve hit a plateau. As I’ve explained in the post on Yuli’s Law, this is a fallacy— the only reason why an illusion of stagnation appears is because computing power is too weak to accomplish the goals of long-time challenges. That, or we have already accomplished said goals a long time ago.

The perfect example of this can be seen with personal computing devices, including PCs, laptops, smartphones— and calculators.

The necessary computing power to run a decent college-ready calculator has long been achieved, and miniaturization has allowed calculators to be sold for pennies.  There is no major quantum leap between calculators and early computer programs.

Calculating the trajectory of a rocket requires far less computing power than some might think, and this is because of the task required: guiding an object using simple algorithms. A second grader could conceivably create a program that guides a bottle rocket in a particular direction.

This is still a step up from purely mechanical systems that give the illusion of programming, but there are obvious limits.

I’ll explain these limits by using a particular example, an example that is the focus of this post: a domestic robot.  Particularly, a Roomba.


An analog domestic robot has no digital programming, so it is beholden to its mechanics. If it is designed to move in a particular direction, it will never move in another direction. In essence, it’s exactly like a wind-up toy.
I will wind up this robot and set it off to clean my floors. Thirty seconds later, it makes a left turn. After it makes this left turn, it will move for twenty seconds before making another left turn. And so on and so forth until it returns to its original spot or runs out of energy.

There are many problems with this. For one, if the Roomba runs into an obstacle, it will not move around it. It will make no attempt to avoid it a second time through. It only moves along a preset path, a path you can perfectly predict the moment you set it off. There is a way to get around this— by adding sensors. Little triggers that will force a turn early if it hits an object.


Let’s bring in a digitally programmed Roomba, something akin to a robot you could have gotten in 2005. Despite having a digital computer for a brain, it seems to act absolutely no different from the mechanical Roomba. It still gets around by bumping into things. Even though the mechanical Roomba could have been created by someone in Ancient Greece, yours doesn’t seem any more impressive on a practical level.

Thus, the robot seems to be more novel than practical. And that’s the perception of Roombas today— cat taxis that clean your floor as a bonus rather than a legitimate domestic robot.
Yet this is no longer a fair perception as the creators of the Roomba, iRobot, have added much-needed intelligence to their machines. This has only been possible thanks to increases in computing power allowing for the proper algorithms to run in real-time.

For example, a 2017-era Roomba 980 can actually “see” in advance when it’s about to run into something and avoid it. It can also remember where it’s been, recognize certain objects, among other things (though Neato’s been able to do this for a long time). Much more impressive, though still not quite what we’re looking for.

What’s going on? Why are robots so weak in an age of reusable space rockets, terabyte smartphones, and popular drone ownership?

We need that last big push. We need computers to be able to understand 3D space.

Imagine a Roomba 2000 from the year 2025. It’s connected to the Cloud and it utilizes the latest in artificial intelligence in order to do a better job than any of its predecessors. I set it down, and the first thing it begins doing is mapping out my home. It recognizes any obstacle as well as any stain— that means if it detects dog poop, it’ll either avoid it or switch to a different suction to pick it up. Once it has mapped my house, it is able to get a good feel for where things are and should be. Of course, I could also send it a picture of another room, and it will still be able to get a feel for what it will need to do even if it’s never roamed around inside before.

The same thing applies to other domestic robots such as robotic lawn mowers— you’d rather have a lawn mower that knows when to stop cutting, whether that means because it’s moving over a new terrain or because it’s approaching your child’s Slip n’ Slide. Without the ability to comprehend 3D space or remember where it’s been or where it needs to go, it’ll be stuck operating within a pre-set invisible fence.

Over all of this, there’s the promise of bipedal and wheeled humanoid robots working in the home. After all, homes are designed around the needs of humans, so it makes sense to design tools modeled after humans. But the same rules apply— no comprehension of 3D space, no dice.

In fact, a universal utility robot like a future model of Atlas or ASIMO will require greater advancements than specialized utility robots like a Roomba or Neato. They must be capable of utilizing tools, including tools they may never have used before. They must be capable of depth perception— a robot that makes the motions of mopping a floor is only useful when you make sure the floor isn’t too closer or far away, but a robot that genuinely knows how to mop is universally useful. They must be capable of understanding natural language so you can give them orders. They must be flexible, in that they can come across new and unknown situations and react to them accordingly. A mechanical robot would come across a small obstacle, fall over, and continue moving its legs. A proper universal utility robot will avoid the obstacle entirely, or at least pick itself up and know to avoid the obstacle and things like it. These are all amazingly difficult problems to overcome at our current technological level.

All these things and more require further improvements in computing power. Improvements were are, indeed, still seeing.

Mother Jones – “Welcome Robot Overlords. Please Don’t Fire Us?”

Passenger Drones

One of the most interesting developments in sci-tech in the past few years is the sudden interest in the concept of “passenger drones“. That appears to be their most popular name, though you may have heard of them as “drone taxis” and “autonomous flying cars”. I’ve even seen the term “pilotless helicopter” used once or twice (though drones don’t necessarily have to be rotored vehicles). For the sake of this article, I’ll stick with ‘passenger drone’.

So what exactly is a passenger drone? In short, its name gives it away— a drone that can carry passengers. Typically, drones are defined as being “unmanned aerial vehicles”. You can see the conflict in definitions, hence why some are hesitant to actually call these ‘drones’. Nevertheless, linguistic drift has changed the definition of drone and that’s something drone hobbyists have to live with.

I say this because passenger drones are based on the designs of quadcopters, now popularly referred to as ‘drones’.

But enough about the linguistics.

Passenger drones represent the closest realization of yesteryear’s dream of flying cars. They are personal vehicles that theoretically anyone can own and use with ease, and they indeed work in three dimensions*. So why should we care about them when that dream has never come true before now?

*”Three dimensions” in transportation terms refers to the inclusion of flight. “Two dimensions” refers to ground and sea travel.

Simple: your answer is in the name. Again.

This is a drone. That means you are not the one piloting the vehicle. And I don’t mean ‘you’ specifically, but ‘you’ as a human. Humans did not evolve to navigate 3D space. We can barely manage traveling in 2D space at high speeds— proto-humans never had to move any faster than their fastest sprint. This becomes obvious when you view motor vehicle statistics. In the United States of America alone, over 30,000 people die in vehicular accidents yearly.
And despite this, we are not even in the top 5 for “most killed yearly in motor accidents.” The number one country is, not surprisingly, China: they lose well over 250,000 a year in car accidents.

Worldwide, 1.25 million die every year in motor accidents. And note, that’s deaths, not casualties. All of this is evidence that humankind is simply not designed well to casually travel at speeds higher than 20 miles per hour.

To throw another dimension and another two hundred miles per hour at us would unleash gigadeaths per year until humanity as a whole finally gives up. Human extinction by flying car.

This is the chief reason why flying cars aren’t a thing. Humans simply cannot handle it. Pilots have to go through thousands of hours of training just to become proficient, and that’s with vehicles that are already highly automated.

Indeed, as of right now, the closest thing to a “flying car” is a Cessna 172.

Of course, other reasons include the fact roadable vehicles and flying vehicles require completely different designs and aerodynamics, as well as the power requirements necessary to keep such a vehicle in the air. But perhaps we could overcome these issues if only there were a way for the common person to actually survive take-off, flight, and landing without killing himself.

Drones are that solution. Take away the need for the common person to do the flying.

That’s the promise passenger drones offer us. Again, there’s still the issue that flying is inefficient, but it’s always possible that passenger drones become a common sight over cities. Perhaps they’ll be privately owned; perhaps they’ll be municipally owned and rented out for use. This remains to be seen because the idea of flying cars and personal aerial vehicles being a real thing only became feasible within the past couple of years.

As of today, 4 April 2017, the first passenger drones will enter operation in Dubai, UAE in the summer of this year.