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Reasons robotics is hard

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Re: Reasons robotics is hard

#71

Post by [deleted] »

cik wrote: post #50
Animats wrote: post #17

A marker of progress will be when Amazon converts to automated picking. They've been trying hard for almost a decade now. They had an annual competition for years. They have a decent picking robot developed in house.[1] It's not being deployed in quantity yet. Nor does it have anything like a humanoid hand. Just a two-surface gripper. Amazon's production robots are mostly automatic guided vehicles, not manipulators.…

Why do we have the assumption that industrial robots need to have legs? The largest grocery store chain uses picker robots already - for building my orders, including fruit / vegetables (from boxes). They currently have humans filter the veggies and fruit prior to box setup, but a friend is working on the models to eliminate even that. Their robots do movement using sliding scale in 3D spaces (think poles that are le…

[deleted]

Re: Reasons robotics is hard

#72

Post by ActorNightly »

kooi wrote: post #6

Yes the problem is very hard. Mainly because high DOF generalization is very difficult. We have self driving cars because what are the control inputs? Pedal, brake, steering wheel. This already took many many years. Now for a humanoid robot: An action space that is metaphorically Hilbert. (Physically, yes, obviously) Also, IMO, LLM's can aid the development of robots, but do little beyond a planning, human control in…

>Yes the problem is very hard. Mainly because high DOF generalization is very difficult.

>We have self driving cars because what are the control inputs? Pedal, brake, steering wheel. This already took many many years.

Its actually amazing to me that this hasn't been solved yet. Its really not that hard of a problem.

Modern robotics, including self driving, are famously all about end-to-end training. We are trying to replicate what humans do through muscle memory. But muscle memory is not what makes us good at operating in the physical world. The thing that matters the most is our ability to simulate the world around us in a compressed form into the future, which lets us predict how our inputs will affect the world.

A similar system in a self driving car should be able to drive perfectly without self inflicted accidents 100% of the time, especially with basic lidar to serve as an error correction mechanism to the camera 3d scene reconstruction.

Re: Reasons robotics is hard

#73

Post by robotresearcher »

i_am_a_peasant wrote: post #66
yen223 wrote: post #65

Earlier quoted context omitted.

It would be nice to have a thing that can carry a bucket of paint up a flight of stairs

you don't need legs to climb stairs, you can have a rotating mechanism with different rows of wheels that have good enough grip, maybe an extruding stick to lift the whole robot up and the wheels move forward... would take a lot fewer joints than pair of legs probably.

If this was awesome, we would have it on wheelchairs. We very rarely do. Stairs are built for legs.

Re: Reasons robotics is hard

#74

Post by pinkmuffinere »

alex43578 wrote: post #51
pinkmuffinere wrote: post #44

> I am confused at how Waymo engineering can be so robust as to yield an astonishingly good safety record, and yet so slapdash as to happily drive into deep water. I feel this is actually somewhat straightforward. I assume deep water on roadways is not commonly in the training set, because frankly it isn't common in real life, and when it is common people do not drive and do not gather that training data. As a result…

People drive into deep water all the time - some states specifically have laws making them financially liable for the cost of rescue because it’s such a stupid thing to do. But still, they do it.

I think the fact that there are laws about it is not good evidence that it should be in the training data. Laws often cover weird edge cases, and if an edge case happens 50 times in 100 years, there's likely a law covering it. At the same time, that's probably not enough occurrences for it to naturally end up in a dataset -- the edge case would probably need to be intentionally sought out. I'm not saying that driving-in-deep water only happens 50 times in 100 years, it's certainly more common than that, I'm just saying that despite laws on the topic, it may still be too rare to be well represented in training data. For example, in real life I've only seen a car drive into deep water once. Even if we include recordings that I've seen, that would maybe bring it up to 20?

Re: Reasons robotics is hard

#75

Post by pinkmuffinere »

snewman wrote: post #62
pinkmuffinere wrote: post #44

> I am confused at how Waymo engineering can be so robust as to yield an astonishingly good safety record, and yet so slapdash as to happily drive into deep water. I feel this is actually somewhat straightforward. I assume deep water on roadways is not commonly in the training set, because frankly it isn't common in real life, and when it is common people do not drive and do not gather that training data. As a result…

(I'm the author of the blog post) My thinking here is that Waymo has logged hundreds of millions of miles at this point (and even more in sim), and there are a lot of nines in their safety record. So even the rare edge cases should have come up. You make a good point that the deep water scenario may be not only rare, but also under-represented in the training data. On the other hand, you'd think they would have thoug…

Ya, this does strike me as pretty high on the list of the thing I'd intentionally seek out if I was doing this job, along with object-in-the-road, icy road, tornado/hurricane, wildfire, and perhaps hail.

Re: Reasons robotics is hard

#76

Post by AngryData »

philot wrote: post #64
AngryData wrote: post #45

This is why im not worried about "AI" taking over the world. Robotics still has a LONG way to go. A human can balance a plate on their arm with food while holding a glass of milk in that hand and a donut in the other and still manage to open a door, step over potential floor obstacles, maneuver tight spaces, get bumped by a child or dog, and still set it all down without spilling it 99% of the time. Just the hardware…

This is true, but it’s also assuming the robot has to be human shaped. A robot with 4 extendable arms and a gyroscopically balanced cabinet in its chest wouldn’t have too much trouble with that task.

How many other tasks is it not suited for now though? If we wanted a device to automate limited specific tasks then we don't need advanced highly advanced robotics. A human has strength, dexterity, high balance, very sensitive tactile touch, can move both fast and slow, is very compact, etc. A robot has to have serious tradeoffs just to hit two of those things. A robot arm that is capable of threading a needle is likely not suited to chopping some wood or carrying groceries. And if you just add a bunch of different arms for different tasks it becomes way more complex, expensive, heavy, and inefficient.

Re: Reasons robotics is hard

#77

Post by chrisjj »

Animats wrote: post #17

A marker of progress will be when Amazon converts to automated picking. They've been trying hard for almost a decade now. They had an annual competition for years. They have a decent picking robot developed in house.[1] It's not being deployed in quantity yet. Nor does it have anything like a humanoid hand. Just a two-surface gripper. Amazon's production robots are mostly automatic guided vehicles, not manipulators.…

> Is Tesla still going to produce vast numbers of humanoid robots by the end of 2026?

Sure, with FSD by 2030 - promise! /s

Re: Reasons robotics is hard

#78

Post by alex43578 »

pinkmuffinere wrote: post #74
alex43578 wrote: post #51

Earlier quoted context omitted.

People drive into deep water all the time - some states specifically have laws making them financially liable for the cost of rescue because it’s such a stupid thing to do. But still, they do it.

I think the fact that there are laws about it is not good evidence that it should be in the training data. Laws often cover weird edge cases, and if an edge case happens 50 times in 100 years, there's likely a law covering it. At the same time, that's probably not enough occurrences for it to naturally end up in a dataset -- the edge case would probably need to be intentionally sought out. I'm not saying that driving…

Oh yeah, I’m not saying it should be in the training data - driving into the water would be a bad data collection strategy.

My point was more that a self-driving car going into water isn’t some unreasonably bad action, as people do it all the time.

If we set the bar for self-driving to be as safe as a human driver, or even 2x as safe, this behavior would still happen.

Re: Reasons robotics is hard

#79

Post by _carbyau_ »

Animats wrote: post #43
_carbyau_ wrote: post #34

People think robots in terms of humanoid or number-5 style robots. I think it'll be more capable appliances at first. Like a lawn mowing device that also spots weeds and can spray them. Next iteration has arms to rip weeds out of the garden. Next has attachments so you can direct it to do pruning. Next it can figure out the pruning itself and move the outcome into the woodchipper. And so on and so on. It's not going…

> Like a lawn mowing device that also spots weeds and can spray them. That's available as a tractor-pulled implement for farms. Deere and some others make such things.

Honestly, I want a little one for my lawn.

Set the boundaries and let it go at midnight to quietly wander the lawn and spray the weeds. If it could spray into nearby garden beds too, so much the better.

Being infinitely patient it could apply small targeted doses to only what you don't want. A human doing it often doesn't have the patience or accuracy.

Re: Reasons robotics is hard

#80

Post by pinkmuffinere »

alex43578 wrote: post #78
pinkmuffinere wrote: post #74

Earlier quoted context omitted.

I think the fact that there are laws about it is not good evidence that it should be in the training data. Laws often cover weird edge cases, and if an edge case happens 50 times in 100 years, there's likely a law covering it. At the same time, that's probably not enough occurrences for it to naturally end up in a dataset -- the edge case would probably need to be intentionally sought out. I'm not saying that driving…

Oh yeah, I’m not saying it should be in the training data - driving into the water would be a bad data collection strategy. My point was more that a self-driving car going into water isn’t some unreasonably bad action, as people do it all the time. If we set the bar for self-driving to be as safe as a human driver, or even 2x as safe, this behavior would still happen.

Ahhhh, that makes sense, that is an interesting point — I think you’re saying it’s just not high risk enough for them to make sure it’s got coverage
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