9 ms·

Boston Dynamics is amazingly impressive as always. But I can't help but feel that they are a lot like the people doing computer vision with hand-written feature

Boston Dynamics is amazingly impressive as always. But I can't help but feel that they are a lot like the people doing computer vision with hand-written features back in the day. It seems inevitable to me that The Bitter Lesson is coming for their control theory approach. (http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html) Possibly the reason it hasn't happened yet is the inaccessibility of robot hardware; nobody else can try different approaches to compete because nobody else has anything like Atlas. Maybe that moat can keep them ahead for a long time, but if the hardware ever becomes cheap enough for real applications, it will be cheap enough for plenty of competition. They will have to adopt more ML methods or be disrupted. Of course maybe they already are, it's not like I actually know how their stuff works in detail, but consensus seems to be that it's still mostly control theory in there.

ricksunny 4y ago
That Bitter Lesson link is an interesting counterpoint to Stephen Wolfram's recent post on WolframAlpha|ChatGPT. Can the world be learned through statistical methods? Clearly it must, as humans did so as a collective. To calculate the distance from Chicago to Tokyo, in the Wolfram approach at one time someone had uploaded Earth_ellipsoid_model and the GPS coordinates of all the world's major cities into Wolfram Alpha. Is doing so falling afoul of the Bitter Lesson?
DoctorOetker 4y ago
what makes you think they are explicitly choreographing each facet of movement? they show they run simulations first, so they first explore a large range of ways to achieve goals, that would be incredibly expensive to manually micromanage. obviously they "compile" the highlevel tasks into lower level "bare metal machine language" ones, to make a metaphor. the people who design a domain specific "compiler" need "intermediate languages" for interpretability and debugging. the fact they give us a peak at their "debugging" capabilities should not be misinterpreted as a bunch of engineers hardcoding in the "assembly language" of robot control. it seems bizarre to view the advanced state of their software tool suite (inspectability, visualizations, ...) as a sign of primitiveness. The subjective experience of becoming aware of the full frontal details of a car when popping open the hood doesn't suddenly demonstrate the "primitiveness" of the car, but rather the subject's prior lack of awareness of how things work...
mrybczyn 4y ago
that's the thing i learned in my robotics class, with a from-scratch robot project. the mechanics are the hardest thing. the electronics the 2nd hardest.
- iancmceachern 4y ago
  And then after you've solved those problems and built some robots you have 2-3 years of software and debugging before anything works reasonably well.
zjaffee 4y ago
I can't speak for right now, as I last worked on this stuff 8 or so years ago, but I briefly contributed to a DARPA robotics challenge team and simply working in ROS as a whole is an immensely difficult process to really get anything to happen with atlas robots. It's not just something a small number of engineers can quickly ramp up on and be immensely productive immediately, you'd need a pretty big team working on stuff over a period of a couple of years to really take things to the next level.
arcastroe 4y ago
There was a recent project that combined transformer breakthroughs (such as those used in ChatGPT) with human motion. [1] https://guytevet.github.io/mdm-page/ https://guytevet.github.io/mdm-page/ [2] https://news.ycombinator.com/item?id=33029522 https://news.ycombinator.com/item?id=33029522 If anyone from Boston Dynamics is reading this, please explore this line of research :) Would be awesome to command Atlas through plain spoken language
- modeless 4y ago
  I bet everyone in robotics is thinking about combining large language models with robot control right now. I expect some pretty awesome demos in the next few years. Robots navigating real spaces and performing actual mundane tasks from natural language commands feels a lot closer than it did a year ago.
  - eternalban 4y ago
    I wonder what NSA and the like are doing with LLMs in context of cryptography. Is there a Shannon information theoretic limit that would make that thought go away? (Doesn't have to be end to end. Could just hugely reduce the search spaces..) -- ps 2008: https://www.cs.unc.edu/~fabian/papers/oakland08.pdf https://www.cs.unc.edu/~fabian/papers/oakland08.pdf
  - mike_hearn 4y ago
    Such demos already exist. I watched one last year. The trick is a (by now) common one: you train the LLM on a simple programming language that's interpreted by the robot's software. So natural language goes in and the LLM then thinks out loud to figure out the needed steps, then emits Logo-like instructions to control where the robot goes. More conventional robotics AI then takes over to handle movement of the machine, the arms, etc.
Snowbird3999 4y ago
Their methods are heavily based on hybrid planning, model-predictive-control, and trajectory optimization which have many parallels to popular ML methodologies [1]. Boston Dynamics problem is not "inaccessibility of robot hardware" or that "they have to adopt more ML methods"... There are more expensive machines in the world that are wildly profitable. Their problem is that it is very very hard to make a business case for humanoid or quadrupedal robots that is not better solved by more standard automation systems that (for example) don't have to be charged every 30 minutes or have to climb up and down the stairs. [1] https://www.youtube.com/watch?v=EGABAx52GKI https://www.youtube.com/watch?v=EGABAx52GKI
- paganel 4y ago
  > problem is that it is very very hard to make a business c Not sure if you can apply the "business" label to it but I'm pretty sure that the US Military is quite happy to throw money at this, no questions asked. Also strange how the guy at end was talking about possible use-cases mentioned stuff like construction but not the battle-fields or suppressing dissent.
- chasd00 4y ago
  A follow-me feature that gets 500lbs of shingles up a ladder and onto a roof would be useful. Granted, probably way more expensive than human labor.
- dclowd9901 4y ago
  This was my thinking. The robot in this video replaced a dumb waiter.
  - geocrasher 4y ago
    With some work, it could probably even replace a smart one.
- modeless 4y ago
  The business case problem is solely a capability problem. Flexibility is hugely valuable and would make a capable humanoid robot way more valuable than traditional automation in plenty of applications. But Atlas just can't do anything useful without decent hands. And it seems to me like the mechanical problems and control problems for hands are actually much harder than for legs. Boston Dynamics doesn't have much going on there yet, at least in public.
deleted 4y ago
[deleted]
agalunar 4y ago
I disagree with The Bitter Lesson, or at least one particular example that the essay gives: chess programming. Modern engines would crush Deep Blue with fewer resources and even without NNUE, and this is because of specific search techniques and search improvements that we've discovered. For example: formulas that work well for late-move reduction; continuation history tables and the update formulas that work well for them; internal iterative reductions; refinements to futility pruning, reverse futility pruning, delta pruning, multi-probcut, and so on. None of these were found by meta methods and none of these emerge from scaling computation. But it's also the case that none of them resemble anything that a human does when they play chess. So I don't think I agree with the first point of the bitter lesson, that general methods beat specific methods, and that scaling beats efficiency. But I do agree with the second point, that performant systems often behave in ways that don't resemble human thinking, and we're often hopeless to personally understand performant systems' complexity.
- sebzim4500 4y ago
  >Modern engines would crush Deep Blue with fewer resources and even without NNUE The point of the essay is that modern engines with NNUE beat modern engines without NNUE. So all those human coded features that used to be used to evaluate positions are no longer necessary.
- cypress66 4y ago
  Chess engines aren't a done deal. Besides NNUE (which I would consider more general) that are outright better than handcrafted eval functions, the dust hasn't settled yet between the more specific AB pruning stockfish vs the more general MCS lc0. Yes, stockfish has been generally slightly stronger, but in the near future with better NN architectures I could easily see this changing. And even then, chess is probably an example of something very narrow that you can deal with specific methods. Humanoid robots are probably the opposite, and you probably need AGI for them.
- GenCuriosity 4y ago
  I'm not an expert in this, so question... Doesn't crush the AI AlphaZero the software Stockfish? Yes, AlphaZero needs more resources - especially for training. But it learns chess from scratch by self playing - in a few hours, without any data from over 1000 years of humankind chess experience. Deep Blue was more like Stockfish than AlphaZero. They called it AI - but its not an AI in the sense of AlphaZero, it was an expert system like Stockfish with special search algorithms. They just needed such a big machine, because this is just like CPU power developed. So I think the term "Bitter Lesson" is a very good match for chess too. A modern AI plays 4 hours from level "very stup... beginner" to "better than any human or machine in the world". It doesn't need a bunch of human super experts, that inject the complete human chess knowledge into specialized databases. This is bitter - isn't it?
intrasight 4y ago
I watched (and even more so heard) his great, great, grandfather hop around in Raibert's lab next door to ours at CMU in 1983. A fellow freshman who worked with him came in to the lab one day with a four inch tall wind up carnival toy that does flips. He said to us that their upgraded $10m pogo stick would soon do flips - and he was correct. Of course my thought at the time was "how do they get funding for this nonsense?" But to see how far Atlas has come in these 40 years is just fucking mind blowing.
- rozab 4y ago
  Video for context: https://youtu.be/XFXj81mvInc https://youtu.be/XFXj81mvInc
  - intrasight 4y ago
    Thanks
kerpotgh 4y ago
[dead]
SergeAx 4y ago
One problem I see in this particular domain is autonomy condition. We can't put that much computing power on board of Atlas/Spot (also minding literally power consumption) and should not make them depend on external computation, because it greatly reduce their application scenarios.
krisoft 4y ago
> nobody else can try different approaches to compete because nobody else has anything like Atlas. But but you know why they have Atlas? They went to a garage and clomped on metal and made it. It is not like some control theorist wandered into the woods and found the right robot. They were building a series of simpler ones, trying their ideas and seeing what works and what doesn’t.
4gotunameagain 4y ago
The "back in the day" hand-written features are still the to go method in safety critical applications and aerospace (autonomous landing etc), excluding of course self driving cars. Sometimes you just cannot replace a provable method with a black box neural net, even if the latter is in fashion.

ricksunny 4y ago

That Bitter Lesson link is an interesting counterpoint to Stephen Wolfram's recent post on WolframAlpha|ChatGPT. Can the world be learned through statistical methods? Clearly it must, as humans did so as a collective. To calculate the distance from Chicago to Tokyo, in the Wolfram approach at one time someone had uploaded Earth_ellipsoid_model and the GPS coordinates of all the world's major cities into Wolfram Alpha. Is doing so falling afoul of the Bitter Lesson?

DoctorOetker 4y ago

what makes you think they are explicitly choreographing each facet of movement? they show they run simulations first, so they first explore a large range of ways to achieve goals, that would be incredibly expensive to manually micromanage. obviously they "compile" the highlevel tasks into lower level "bare metal machine language" ones, to make a metaphor. the people who design a domain specific "compiler" need "intermediate languages" for interpretability and debugging. the fact they give us a peak at their "debugging" capabilities should not be misinterpreted as a bunch of engineers hardcoding in the "assembly language" of robot control. it seems bizarre to view the advanced state of their software tool suite (inspectability, visualizations, ...) as a sign of primitiveness. The subjective experience of becoming aware of the full frontal details of a car when popping open the hood doesn't suddenly demonstrate the "primitiveness" of the car, but rather the subject's prior lack of awareness of how things work...

mrybczyn 4y ago

that's the thing i learned in my robotics class, with a from-scratch robot project. the mechanics are the hardest thing. the electronics the 2nd hardest.

iancmceachern 4y ago
And then after you've solved those problems and built some robots you have 2-3 years of software and debugging before anything works reasonably well.

zjaffee 4y ago

I can't speak for right now, as I last worked on this stuff 8 or so years ago, but I briefly contributed to a DARPA robotics challenge team and simply working in ROS as a whole is an immensely difficult process to really get anything to happen with atlas robots. It's not just something a small number of engineers can quickly ramp up on and be immensely productive immediately, you'd need a pretty big team working on stuff over a period of a couple of years to really take things to the next level.

arcastroe 4y ago

There was a recent project that combined transformer breakthroughs (such as those used in ChatGPT) with human motion. [1] https://guytevet.github.io/mdm-page/ https://guytevet.github.io/mdm-page/ [2] https://news.ycombinator.com/item?id=33029522 https://news.ycombinator.com/item?id=33029522 If anyone from Boston Dynamics is reading this, please explore this line of research :) Would be awesome to command Atlas through plain spoken language

modeless 4y ago
I bet everyone in robotics is thinking about combining large language models with robot control right now. I expect some pretty awesome demos in the next few years. Robots navigating real spaces and performing actual mundane tasks from natural language commands feels a lot closer than it did a year ago.
- eternalban 4y ago
  I wonder what NSA and the like are doing with LLMs in context of cryptography. Is there a Shannon information theoretic limit that would make that thought go away? (Doesn't have to be end to end. Could just hugely reduce the search spaces..) -- ps 2008: https://www.cs.unc.edu/~fabian/papers/oakland08.pdf https://www.cs.unc.edu/~fabian/papers/oakland08.pdf
- mike_hearn 4y ago
  Such demos already exist. I watched one last year. The trick is a (by now) common one: you train the LLM on a simple programming language that's interpreted by the robot's software. So natural language goes in and the LLM then thinks out loud to figure out the needed steps, then emits Logo-like instructions to control where the robot goes. More conventional robotics AI then takes over to handle movement of the machine, the arms, etc.

Snowbird3999 4y ago

Their methods are heavily based on hybrid planning, model-predictive-control, and trajectory optimization which have many parallels to popular ML methodologies [1]. Boston Dynamics problem is not "inaccessibility of robot hardware" or that "they have to adopt more ML methods"... There are more expensive machines in the world that are wildly profitable. Their problem is that it is very very hard to make a business case for humanoid or quadrupedal robots that is not better solved by more standard automation systems that (for example) don't have to be charged every 30 minutes or have to climb up and down the stairs. [1] https://www.youtube.com/watch?v=EGABAx52GKI https://www.youtube.com/watch?v=EGABAx52GKI

paganel 4y ago
> problem is that it is very very hard to make a business c Not sure if you can apply the "business" label to it but I'm pretty sure that the US Military is quite happy to throw money at this, no questions asked. Also strange how the guy at end was talking about possible use-cases mentioned stuff like construction but not the battle-fields or suppressing dissent.
chasd00 4y ago
A follow-me feature that gets 500lbs of shingles up a ladder and onto a roof would be useful. Granted, probably way more expensive than human labor.
dclowd9901 4y ago
This was my thinking. The robot in this video replaced a dumb waiter.
- geocrasher 4y ago
  With some work, it could probably even replace a smart one.
modeless 4y ago
The business case problem is solely a capability problem. Flexibility is hugely valuable and would make a capable humanoid robot way more valuable than traditional automation in plenty of applications. But Atlas just can't do anything useful without decent hands. And it seems to me like the mechanical problems and control problems for hands are actually much harder than for legs. Boston Dynamics doesn't have much going on there yet, at least in public.

deleted 4y ago

[deleted]

agalunar 4y ago

I disagree with The Bitter Lesson, or at least one particular example that the essay gives: chess programming. Modern engines would crush Deep Blue with fewer resources and even without NNUE, and this is because of specific search techniques and search improvements that we've discovered. For example: formulas that work well for late-move reduction; continuation history tables and the update formulas that work well for them; internal iterative reductions; refinements to futility pruning, reverse futility pruning, delta pruning, multi-probcut, and so on. None of these were found by meta methods and none of these emerge from scaling computation. But it's also the case that none of them resemble anything that a human does when they play chess. So I don't think I agree with the first point of the bitter lesson, that general methods beat specific methods, and that scaling beats efficiency. But I do agree with the second point, that performant systems often behave in ways that don't resemble human thinking, and we're often hopeless to personally understand performant systems' complexity.

sebzim4500 4y ago
>Modern engines would crush Deep Blue with fewer resources and even without NNUE The point of the essay is that modern engines with NNUE beat modern engines without NNUE. So all those human coded features that used to be used to evaluate positions are no longer necessary.
cypress66 4y ago
Chess engines aren't a done deal. Besides NNUE (which I would consider more general) that are outright better than handcrafted eval functions, the dust hasn't settled yet between the more specific AB pruning stockfish vs the more general MCS lc0. Yes, stockfish has been generally slightly stronger, but in the near future with better NN architectures I could easily see this changing. And even then, chess is probably an example of something very narrow that you can deal with specific methods. Humanoid robots are probably the opposite, and you probably need AGI for them.
GenCuriosity 4y ago
I'm not an expert in this, so question... Doesn't crush the AI AlphaZero the software Stockfish? Yes, AlphaZero needs more resources - especially for training. But it learns chess from scratch by self playing - in a few hours, without any data from over 1000 years of humankind chess experience. Deep Blue was more like Stockfish than AlphaZero. They called it AI - but its not an AI in the sense of AlphaZero, it was an expert system like Stockfish with special search algorithms. They just needed such a big machine, because this is just like CPU power developed. So I think the term "Bitter Lesson" is a very good match for chess too. A modern AI plays 4 hours from level "very stup... beginner" to "better than any human or machine in the world". It doesn't need a bunch of human super experts, that inject the complete human chess knowledge into specialized databases. This is bitter - isn't it?

intrasight 4y ago

I watched (and even more so heard) his great, great, grandfather hop around in Raibert's lab next door to ours at CMU in 1983. A fellow freshman who worked with him came in to the lab one day with a four inch tall wind up carnival toy that does flips. He said to us that their upgraded $10m pogo stick would soon do flips - and he was correct. Of course my thought at the time was "how do they get funding for this nonsense?" But to see how far Atlas has come in these 40 years is just fucking mind blowing.

rozab 4y ago
Video for context: https://youtu.be/XFXj81mvInc https://youtu.be/XFXj81mvInc
- intrasight 4y ago
  Thanks

kerpotgh 4y ago

[dead]

SergeAx 4y ago

One problem I see in this particular domain is autonomy condition. We can't put that much computing power on board of Atlas/Spot (also minding literally power consumption) and should not make them depend on external computation, because it greatly reduce their application scenarios.

krisoft 4y ago

> nobody else can try different approaches to compete because nobody else has anything like Atlas. But but you know why they have Atlas? They went to a garage and clomped on metal and made it. It is not like some control theorist wandered into the woods and found the right robot. They were building a series of simpler ones, trying their ideas and seeing what works and what doesn’t.

4gotunameagain 4y ago

The "back in the day" hand-written features are still the to go method in safety critical applications and aerospace (autonomous landing etc), excluding of course self driving cars. Sometimes you just cannot replace a provable method with a black box neural net, even if the latter is in fashion.