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Common misconceptions about the complexity in robotics vs. AI (2024)

154 points by wallflower 2y ago

jvanderbot 2y ago
> Moravec’s paradox is the observation by artificial intelligence and robotics researchers that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources. The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky, and others in the 1980s. I have a name for it now! I've said over and over that there are only two really hard problems in robotics: Perception and funding. A perfectly perceived system and world can be trivially planned for and (at least proprio-)controlled. Imagine having a perfect intuition about other actors such that you know their paths (in self driving cars), or your map is a perfect voxel + trajectory + classification. How divine! It's limited information and difficulties in reducing signal to concise representation that always get ya. This is why the perfect lab demos always fail - there's a corner case not in your training data, or the sensor stuttered or became misaligned, or etc etc.
- jvanderbot 2y ago
  > Moravec hypothesized around his paradox, that the reason for the paradox [that things we perceive as easy b/c we dont think about them are actually hard] could be due to the sensor & motor portion of the human brain having had billions of years of experience and natural selection to fine-tune it, while abstract thoughts have had maybe 100 thousand years or less Another gem!
  - Legend2440 2y ago
    Or it could be a parallel vs serial compute thing. Perception tasks involve relatively simple operations across very large amounts of data, which is very easy if you have a lot of parallel processors. Abstract thought is mostly a serial task, applying very complex operations to a small amount of data. Many abstract tasks like evaluating logical expressions cannot be done in parallel - they are in the complexity class P-complete. Your brain is mostly a parallel processor (80 billion neurons operating asynchronously), so logical reasoning is hard and perception is easy. Your CPU is mostly a serial processor, so logical reasoning is easy and perception is hard.
    - cratermoon 2y ago
      > Perception tasks involve relatively simple operations across very large amounts of data, which is very easy if you have a lot of parallel processors. Yes, relatively simple. Wait, isn't that exactly what the article explained was completely wrong-headed?
      - burnished 2y ago
        No. The article is talking about things we think of as being easy because they are easy for a human to perform but that are actually very difficult to formalize/reproduce artificially. The person you are responding to is instead comparing differences in biological systems and mechanical systems.
    - visarga 2y ago
      > Or it could be a parallel vs serial compute thing. The brain itself is both a parallel system an a serially constrained system. It has distributed activity but it must resolve in a serial chain of action. We can't walk left and right at the same time. Any goals forces us to follow specific steps in specific order. This conflict between parallel processing and serial outputs is where the magic happens.
  - topherclay 2y ago
    > ...the sensor & motor portion of the human brain having had billions of years of experience. It doesn't really change the significance of the quote, but I can't help but point out that we didn't even have nerve cells more than 0.6 billion of years ago.
- lang4d 2y ago
  Maybe just semantics, but I think I would call that prediction. Even if you have perfect perception (measuring the current state of the world perfectly), it's nontrivial to predict the future paths of other actors. The prediction problem requires intuition about what the other actors are thinking, how their plans influence each other, and how your plan influences them.
- bobsomers 2y ago
  > I've said over and over that there are only two really hard problems in robotics: Perception and funding. A perfectly perceived system and world can be trivially planned for and (at least proprio-)controlled. Funding for sure. :) But as for perception, the inverse is also true. If I have an perfect planning/prediction system, I can throw the grungiest, worst perception data into it and it will still plan successfully despite tons of uncertainty. And therein lies the real challenge of robotics: It's fundamentally a systems engineering problem. You will never have perfect perception or a perfect planner. So, can you make a perception system that is good enough that, when coupled with your planning system which is good enough, you are able to solve enough problems with enough 9s to make it successful. The most commercially successful robots I've seen have had some of the smartest systems engineering behind them, such that entire classes of failures were eliminated by being smarter about what you actually need to do to solve the problem and aggressively avoid solving subproblems that aren't absolutely necessary. Only then do you really have a hope of getting good enough at that focused domain to ship something before the money runs out. :)
  - portaouflop 2y ago
    > being smarter about what you actually need to do to solve the problem and aggressively avoid solving subproblems that aren't absolutely necessary I feel like this is true for every engineering discipline or maybe even every field that needs to operate in the real world
    - vrighter 2y ago
      except software, of course. Nowadays it seems that software is all about creating problems to create solutions for.
  - krisoft 2y ago
    > If I have an perfect planning/prediction system, I can throw the grungiest, worst perception data into it and it will still plan successfully despite tons of uncertainty. Not really. Even the perfect planning system will appear eratic in the presence of perception noise. It must be because it can’t create information out of nowhere. I have seen robots eratically stop because they thought that the traffic in the oncomming lane is enroaching on theirs. You can’t make the planning system ignore that because then sometimes it will collide with people playing chicken with you. Likewise I have seen robots eratically stop because they thought that a lamp post was slowly reversing out in front of them. All due to perception noise (in this case both location noise, and misclassification.) And do note that these are just the false positives. If you have a bad perception system you can also suffer from false negatives. Just experiment biases hide those. So your “perfect planning/prediction” will appear overly cautious while at the same time will be sometimes reckless. Because it doesn’t have the information to not to. You can’t magic plan your way out of that. (Unless you pipe the raw sensor data into the planner, in which case you created a second perception system you are just not calling it perception.)
- exe34 2y ago
  "the sensor stuttered or became misaligned, or etc etc." if your eyes suddenly crossed, you'd probably fall over too!
- seanhunter 2y ago
  Yeah the fun way Moravec's paradox was explained to me [1] is that you can now easily get a computer to solve simultaneous differential equations governing all the axes of motion of a robot arm but getting it to pick one screw out of a box of screws is an unsolved research problem. [1] by a disillusioned computer vision phd that left the field in the 1990s.
  - wrp 2y ago
    Selective attention was one of the main factors in Hubert Dreyfus' explanation of "what computers can't do." He had a special term for it, which I can't remember off-hand.
- visarga 2y ago
  > A perfectly perceived system and world can be trivially planned for I think it's not about perfect perception, there is no such thing not even in humans, it's about adaptability, recovery from error, resilience, and mostly about learning from the outside when the process fails to work. Each problem has its own problem space to explore. I think of intelligence as search efficiency across many problem spaces, there is no perfection in it. Our problem spaces are far from exhaustively known.
catgary 2y ago
Yeah, this was my general impression after a brief, disastrous stretch in robotics after my PhD. Hell, I work in animation now, which is a way easier problem since there are no physical constraints, and we still can’t solve a lot of the problems the OP brings up. Even stuff like using video misses the point, because so much of our experience is via touch.
- johnwalkr 2y ago
  I've worked in a robotics-adjacent field for 15 years and robotics is truly hard. The number of people and companies I've seen come and go that claim their software expertise will make a useful, profitable robot is.. a lot.
Legend2440 2y ago
Honestly I'm tired of people who are more focused on 'debunking the hype' than figuring out how to make things work. Yes, robotics is hard, and it's still hard despite big breakthroughs in other parts of AI like computer vision and NLP. But deep learning is still the most promising avenue for general-purpose robots, and it's hard to imagine a way to handle the open-ended complexity of the real world other than learning. Just let them cook.
- mitthrowaway2 2y ago
  > If you want a more technical, serious (better) post with a solution oriented point to make, I’ll refer you to Eric Jang’s post [1] [1] https://evjang.com/2022/07/23/robotics-generative.html https://evjang.com/2022/07/23/robotics-generative.html
- FloorEgg 2y ago
  As someone on the sidelines of robotics who generally feels everything getting disrupted and at the precipice of major change, it's really helpful to have a clearer understanding of the actual challenge and how close we are to solving it. Anything that helps me make more accurate predictions will help me make better decisions about what problems I should be trying to solve and what skills I should be trying to develop.
cratermoon 2y ago
It might be nice if the author qualified "most of the freely available data on the internet" with "whether or not it was copyrighted" or something to acknowledge the widespread theft of the works of millions.
- danielbln 2y ago
  Theft is the wrong term, it implies that the original is no longer available. It's copyright infringement at best, and possibly fair use depending on jurisdiction. It wasn't theft when the RIAA went on a lawsuit spree against mp3 copying, and it isn't theft now.
  - CaptainFever 2y ago
    Related: https://www.youtube.com/watch?v=IeTybKL1pM4 https://www.youtube.com/watch?v=IeTybKL1pM4
  - cratermoon 2y ago
    Ackchyually....
- deleted 2y ago
  [deleted]
jes5199 2y ago
I would love to see some numbers. How many orders of magnitude more complicated do we think embodiment is, compared to conversation? How much data do we need compared to what we’ve already collected?
- FloorEgg 2y ago
  If nature computed both through evolution, then maybe it's approximately the same ratio. So roughly the time it took to evolve embodiment, and roughly the time it took to evolve from grunts to advanced language. If we start from when we think multicellular life first evolved (~2b years), or maybe the Cambrian explosion (~500m years), and until modern humans (~300k years). Then compare that to the time between first modern humans now now. It seems like maybe 3-4 orders of magnitude harder. My intuition after reading the articles is that there needs to be way more sensors all throughout the robot, probably with lots of redundancies, and then lots of modern LLM sized models all dedicated to specific joints and functions and capable of cascading judgement between each other, similar to how our nervous system works.
  - jes5199 2y ago
    so like ten to twenty years, via moore’s law?
    - daveguy 2y ago
      Maybe. If Moore's law remotely holds up for ten to twenty years. There's still the part about not having a clue how to replicate physical systems efficiently vs logical systems.
- rstuart4133 2y ago
  "Hardness" is a difficult quantity to define if you venture beyond "humans have been trying to build systems to do this for a while, and haven't succeeded". Insects have succeed in build precision systems that combine vision, smell, touch and a few other senses. I doubt finding a juicy spider, immobilising it, is that much more difficult that finding a door knob and turning it, or folding a T-Shirt. Yet insects accomplish it with I suspect far less compute than modern LLM's. So it's not "hard" in the sense of requiring huge compute resources, and certainly not a lot of power. So it's probably not that hard in the sense that it's well within the capabilities of the hardware we have now. The issue is more that we don't have a clue how to do it.
no_op 2y ago
I think Moravec's Paradox is often misapplied when considering LLMs vs. robotics. It's true that formal reasoning over unambiguous problem representations is easy and computationally cheap. Lisp machines were already doing this sort of thing in the '70s. But the kind of commonsense reasoning over ambiguous natural language that LLMs can do is not easy or computationally cheap. Many early AI researchers thought it would be — that it would just require a bit of elaboration on the formal reasoning stuff — but this was totally wrong. So, it doesn't make sense to say that what LLMs do is Moravec-easy, and therefore can't be extrapolated to predict near-term progress on Moravec-hard problems like robotics. What LLMs do is, in fact, Moravec-hard. And we should expect that if we've got enough compute to make major progress on one Moravec-hard problem, there's a good chance we're closing in on having enough to make major progress on others.
- bjornsing 2y ago
  Good points. Came here to say pretty much the same. Moravec's Paradox is certainly interesting and correct if you limit its scope (as you say). But it feels intuitively wrong to me to make any claims about the relative computational demands of sensi-motor control and abstract thinking before we’ve really solved either problem. Looking e.g. at the recent progress in solving ARC-AGI my impression is that abstract thought could have incredible computational demands. IIRC they had to throw approximately $10k of compute at o3 before it reached human performance. Now compare how cognitively challenging ARC-AGI is to e.g. designing or reorganizing a Tesla gigafactory. With that said I do agree that our culture tends to value simple office work over skillful practical work. Hopefully the progress in AI/ML will soon correct that wrong.
  - RaftPeople 2y ago
    Also agree and also came here to say the same.
- lsy 2y ago
  Leaving aside the lack of consensus around whether LLMs actually succeed in commonsense reasoning, this seems a little bit like saying “Actually, the first 90% of our project took an enormous amount of time, so it must be ‘Pareto-hard’. And thus the last 10% is well within reach!” That is, that Pareto and Moravec were in fact just wrong, and thing A and thing B are equivalently hard. Keeping the paradox would more logically bring you to the conclusion that LLMs’ massive computational needs and limited capacities imply a commensurately greater, mind-bogglingly large computational requirement for physical aptitude.
jillesvangurp 2y ago
Yesterday, I was watching some of the youtube videos on the website of a robotics company https://www.figure.ai https://www.figure.ai that challenges some of the points in this article a bit. They have a nice robot prototype that (assuming these demos aren't faked) does fairly complicated things. And one of the key features they show case is using OpenAI's AI for the human computer interaction and reasoning. While these things seem a bit slow, they do get things done. They have a cool demo of the a human interacting with one of the prototypes to ask it what it thinks needs to be done and then asking it do these things. That show cases reasoning, planning, and machine vision. Which are exactly topics that all the big LLM companies are working on. They appear to be using an agentic approach similar to how LLMs are currently being integrated into other software products. Honestly, it doesn't even look like they are doing much that isn't part of OpenAI's APIs. Which is impressive. I saw speech capabilities, reasoning, visual inputs, function calls, etc. in action. Including the dreaded "thinking" pause where the Robot waits a few seconds for the remote GPUs to do their thing. This is not about fine motor control but about replacing humans controlling robots with LLMs controlling robots and getting similarly good/ok results. As the article argues, the hardware is actually not perfect but good enough for a lot of tasks if it is controlled by a human. The hardware in this video is nothing special. Multiple companies have similar or better prototypes. Dexterity and balance are alright but probably not best in class. Best in class hardware is not the point of these demos. Dexterity and real time feedback is less important than the reasoning and classification capabilities people have. The latency just means things go a bit slower. Watching these things shuffle around like an old person that needs to go to the bath room is a bit painful. But getting from A to B seems like a solved problem. A 2 or 3x speedup would be nice. 10x would be impressively fast. 100x would be scary and intimidating to have near you. I don't think that's going to be a challenge long term. Making LLMs faster is an easier problem than making them smarter. Putting a coffee cup in a coffee machine (one of the demo videos) and then learning to fix it when it misaligns seems like an impressive capability. It compensates for precision and speed with adaptability and reasoning: analyze the camera input, correctly analyze the situation, problem and challenge come up with a plan to perform the task, execute the plan, re-evaluate, adapt, fix. It's a bit clumsy but the end result is coffee. Good demo and I can see how you might make it do all sorts of things that are vaguely useful that way. The key point here is that knowing that the thing in front of the robot is a coffee cup and a coffee machine and identifying how those things fit together and in what context that is required are all things that LLMs can do. Better feedback loops and hardware will make this faster, and less tedious to watch. Faster LLMs will help with that too. And better LLMs will result in less mistakes, better plans, etc. It seems both capabilities are improving at an enormously fast pace right now. And a fine point with human intelligence is that we divide and conquer. Juggling is a lot harder when you start thinking about it. The thinking parts of your brain interferes with the lower level neural circuits involved with juggling. You'll drop the balls. The whole point with juggling is that you need to act faster than you can think. Like LLMs, we're too slow. But we can still learn to juggle. Juggling robots are going to be a thing.
jonas21 2y ago
It's worth noting that modern multimodal models are not confused by the cat image. For example, Claude 3.5 Sonnet says: > This image shows two cats cuddling or sleeping together on what appears to be a blue fabric surface, possibly a blanket or bedspread. One cat appears to be black while the other is white with pink ears. They're lying close together, suggesting they're comfortable with each other. The composition is quite sweet and peaceful, capturing a tender moment between these feline companions.
- throw310822 2y ago
  Also Claude, when given the entire picture: "This is a humorous post showcasing an AI image recognition system making an amusing mistake. The neural network (named "neural net guesses memes") attempted to classify an image with 99.52% confidence that it shows a skunk. However, the image actually shows two cats lying together - one black and one white - whose coloring and positioning resembles the distinctive black and white pattern of a skunk. The humor comes from the fact that while the AI was very confident (99.52%) in its prediction, it was completely wrong..." The progress we made in barely ten years is astounding.
  - timomaxgalvin 2y ago
    It's easy to make something work when the example goes from being out of the training data to into the training data.
    - throw310822 2y ago
      Definitely. But I also tried with a picture of an absurdist cartoon drawn by a family member, complete with (carefully) handwritten text, and the analysis was absolutely perfect.
      - visarga 2y ago
        A simple test - take one of your own photos, something interesting, and put in into a LLM, let it describe it in words. Then use a image generator to create the image back. It works like back-translation image->text->image. It proves how much the models really understand images and text.
bjornsing 2y ago
I’m surprised this doesn’t place more emphasis on self-supervised learning through exploration. Is human-labeled datasets really the SOTA approach for robotics?
- psb217 2y ago
  Human-labeled data currently looks like the quickest path to making robots that are useful enough to have economic value beyond settings and tasks that are explicitly designed for robots. This has drawn a lot of corporate and academic research activity away from solving the harder core problems, like exploration, that are critical for developing fully autonomous intelligent agents.
MrsPeaches 2y ago
Question: Isn’t it fundamentally impossible to model a highly entropic system using deterministic methods? My point is that animal brains are entropic and “designed” to model entropic systems, where as computers are deterministic and actively have to have problems reframed as deterministic so that they can solve them. All of the issues mentioned in the article boil down to the fundamental problem of trying to get deterministic systems to function in highly entropic environments. LLMs are working with language, which has some entropy but is fundamentally a low entropy system, and has orders of magnitude less entropy than most peoples’ back garden! As the saying goes, to someone with a hammer, everything looks like a nail.
- BlueTemplar 2y ago
  Not fundamentally, at least I doubt it : pseudo-random number generation is technically deterministic. And it's used for sampling these low information systems that you are mentioning. (And let's not also forget how they are helpful in sampling deterministic but extremely high complexity systems involving a high amount of dimensions that Monte Carlo methods are so good at dealing with.)
Peteragain 2y ago
So I'm old. PhD on search engines in the early 1990's (yep, early 90s). Learnt AI in the dark days of the 80's. So, there is an awful lotl of forgetting going on, largely driven by the publish-or-perish culture we have. Brooks' subsumption architecture was not perfect, but it outlined an approach that philosophy and others have been championing for decades. He said he was not implementing Heidegger, just doing engineering, but Brooks was certainly channeling Heidegger's successors. Subsumption might not scale, but perhaps that is where ML comes in. On a related point, "generative AI" does sequences (it's glorified auto complete (not) according to Hinton in the New Yorker). Data is given to a Tokeniser that produces a sequence of tokens, and the "AI" predicts what comes next. Cool. Robots are agents in an environment with an Umwelt. Robotics is pre the Tokeniser. What is it the is recognisable and sequential in the world? 2 cents please.
- redlock 2y ago
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- marcosdumay 2y ago
  > Subsumption might not scale Honestly, I don't think we have any viable alternative. And anyway, it seems to scale well enough that we use "conscious" and "unconscious" decisions ourselves.
  - psb217 2y ago
    If you wanna sound hip, you need to call it "system 2" and "system 1".
Anotheroneagain 2y ago
The reason why it sounds counterintuitive is that neurology has the brain upside down. It teaches us that formal thinking occurs in the neocortex, and we need all that huge brain mass for that. But in fact it works like an autoencoder, and it reduces sensory inputs into a much smaller latent space, or something very similar to that. This does result in holistic and abstract thinking, but formal analytical thinking doesn't require abstraction to do the math or to follow a method without comprehension. It's a concrete approach that avoids the need for abstraction. The cerebellum is the statistical machine that gets measured by IQ and other tests. To further support that, you don't see any particularly elegant motions from non mammal animals. In fact everything else looks quite clumsy, and even birds need to figure out flying by trial and error.
- daveguy 2y ago
  Claiming to know how the brain works, computationally or physically, might be a bit premature.
dbspin 2y ago
I find it odd that the article doesn't address the apparent success of training with transformer based models in virtual environments to build models that are then mapped onto the real world. This is being used in everything from building datasets for self driving cars, to navigation and task completion for humanoid robots. Nvidia have their omniverse project [1], but there are countless other examples [2][3][4]. Isn't this obviously the way to build the corpus of experience needed to train these kinds of cross modal models? [1] https://www.nvidia.com/en-us/industries/robotics/#:~:text=NVIDIA%20Omniverse%E2%80%AC%20is%20a,the%20transfer%20of%20learned%20behavior https://www.nvidia.com/en-us/industries/robotics/#:~:text=NV.... [2] https://www.sciencedirect.com/science/article/abs/pii/S009784932100011X https://www.sciencedirect.com/science/article/abs/pii/S00978... [3] https://techcrunch.com/2024/01/04/google-outlines-new-methods-for-training-robots-with-video-and-large-language-models/ https://techcrunch.com/2024/01/04/google-outlines-new-method... [4] https://techxplore.com/news/2024-09-google-deepmind-unveils-ai-based.html https://techxplore.com/news/2024-09-google-deepmind-unveils-...
- cybernoodles 2y ago
  A common practice is to train a transformer model to control a given robot model in simulation by first teleoperating the simulated model with some controller (keyboard, joystick, etc.) to complete the task and create a dataset, and then setting up the simulator to permute the environment variables such as frictions, textures, etc (domain randomization) and run many epochs at faster than real time until a final policy converges. If the right things were randomized and your demonstration examples provided enough variation of information, it should generalize well to the actual hardware.
CWIZO 2y ago
> Robots are probably amazed by our ability to keep a food tray steady, the same way we are amazed by spider-senses (from spiderman movie) Funnily, Toby Maguire actually did that tray catching stunt for real. So robots have an even further way to go. https://screenrant.com/spiderman-sam-raimi-peter-parker-tray-catch-no-cgi/ https://screenrant.com/spiderman-sam-raimi-peter-parker-tray...
- BlueTemplar 2y ago
  ... but it took 156 takes as well as some glue. And, as the article insists on, for robots to be acceptable, it's more like they need to get to a point where they fail 1 time in 156 (or even less, depending on how critical the failure is), rather than succeed 1 time in 156...
PeterStuer 2y ago
Just some observations from an ex autonomous robotics researcher here. One of the most important differences at least in those days (80's and 90's) was time. While the digital can be sped up just constrained by the speed of your compute, the 'real world' is very constrained by real time physics. You can't speed up a robot 10x in a 10.000 grabbing and stacking learning run without completely changing the dynamics. Also, parallellizing the work requires more expensive full robots rather than more compute cores. Maybe these days the different ai gym like virtual physics environments offer a (partial) solution to that problem, but I have not used them (yet) so I can't tell. Furthermore, large scale physical robots are far more fragile due to wear and tear than the incredible resilience of modern compute hardware. Getting a perfect copy of a physical robot and environment is a very hard, near impossible, task. Observability and replay, while trivial in the digital world, is very limited in the physical environment making analysis much more difficult. I was both excited and frustrated at the time by making ai do more than rearanging pixels on a 2D surface. Good times were had.
bsenftner 2y ago
This struck me as a universal truth: "our general intuition about the difficulty of a problem is often a bad metric for how hard it actually is". I feel like this is the core issue of all engineering, all our careers, and was surprised by the logic leap from that immediately to Moravec's Paradox, from a universal truth to a myopic industry insight. Although I've not done physical robotics, I've done a lot of articulated human animation of independent characters in 3D animation. His insight that motor control is more difficult sets right with me.
Havoc 2y ago
Fun fact: that Spider-Man gif in there - it’s real. No CGI
cameldrv 2y ago
Moravec's paradox is really interesting in terms of what it says about ourselves: We are super impressive in ways in which we aren't consciously aware. My belief about this is that our self-aware mind is only a very small part of what our brain is doing. This is extremely clear when it comes to athletic performance, but also there are intellectual things that people call intuition or other things, which aren't part of our self-aware mind, but still do a ton of heavy lifting in our day to day life.
NalNezumi 2y ago
Oh weird to wake up to see something I wrote more than half year ago (and posted on HN with no traction) getting reposted now. Glad to see so many different takes on it. It was written in slight jest as a discussion starter with my ML/neuroscience coworker and friends, so it's actually very insightful to see some rebuttals. Initial post was twice the length, and had several more (in retrospect) interesting points. First ever blog post so reading it now fills me with cringe. Some stuff have changed in only half year, so will see if the points stands the test of time ;]
- bo1024 2y ago
  It's a good post, nice work.
lugu 2y ago
I think one problem is composition. Computer multiplex access to CPU and memory, but this strategy doesn't work for actuator and sensors. That is why we see great demos of robots doing one thing. The hard part is to make them do multiple things at the same time.
gcanyon 2y ago
> “Everyone equates the Skynet with the T900 terminator, but those are two very different problems with different solutions.” while this is my personal opinion, the latter one (T900) is a harder problem. So based on this, Skynet had to hide and wait for years before being able to successfully revolt against the humans...
lairv 2y ago
This post didn't really convince me that robotics is inherently harder than generating text or images On the one hand we have problems where ~7B humans have been generating data for 30 years every day (more if you count old books), on the other hand we have a problem where researcher are working with ~1000 human collected trajectories (I think the largest existing dataset is OXE with ~1M trajectories: https://robotics-transformer-x.github.io/ https://robotics-transformer-x.github.io/ ) Web-scale datasets for LLMs benefits from a natural diversity, they're not highly correlated samples generated by contractors or researchers in academic labs. In the largest OXE dataset, what do you think is the likelihood that there is a sample where a robot picks up a rock from the ground and throws it in a lake? Close to zero, because tele-operated data comes from a very constrained data distribution. Another problem is that robotics doesn't have an easy universal representation for its data. Let's say we were able to collect web-scale dataset for one particular robot A with high diversity, how would it transfer to robot B with a slightly different design? Probably poorly, so not only does the data distribution needs to cover a high range of behavior, it must also cover a high range of embodiment/hardware With that being said, I think it's fair to say that collecting large scale dataset for general robotics is much harder than collecting text or images (at least in the current state of humanity)
lincpa 2y ago
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K0balt 2y ago
While my knee jerk to this is “errr… hogwash” there might be something to it if you imagine consciousness not as an internal state but rather as something that an observer imposes upon the universe. This might be true if the act of observing is what determines that which can be observed, and there is some evidence that this might be the case.

154 points by wallflower 2y ago

> Moravec’s paradox is the observation by artificial intelligence and robotics researchers that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources. The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky, and others in the 1980s. I have a name for it now! I've said over and over that there are only two really hard problems in robotics: Perception and funding. A perfectly perceived system and world can be trivially planned for and (at least proprio-)controlled. Imagine having a perfect intuition about other actors such that you know their paths (in self driving cars), or your map is a perfect voxel + trajectory + classification. How divine! It's limited information and difficulties in reducing signal to concise representation that always get ya. This is why the perfect lab demos always fail - there's a corner case not in your training data, or the sensor stuttered or became misaligned, or etc etc.

jvanderbot 2y ago
> Moravec hypothesized around his paradox, that the reason for the paradox [that things we perceive as easy b/c we dont think about them are actually hard] could be due to the sensor & motor portion of the human brain having had billions of years of experience and natural selection to fine-tune it, while abstract thoughts have had maybe 100 thousand years or less Another gem!
- Legend2440 2y ago
  Or it could be a parallel vs serial compute thing. Perception tasks involve relatively simple operations across very large amounts of data, which is very easy if you have a lot of parallel processors. Abstract thought is mostly a serial task, applying very complex operations to a small amount of data. Many abstract tasks like evaluating logical expressions cannot be done in parallel - they are in the complexity class P-complete. Your brain is mostly a parallel processor (80 billion neurons operating asynchronously), so logical reasoning is hard and perception is easy. Your CPU is mostly a serial processor, so logical reasoning is easy and perception is hard.
  - cratermoon 2y ago
    > Perception tasks involve relatively simple operations across very large amounts of data, which is very easy if you have a lot of parallel processors. Yes, relatively simple. Wait, isn't that exactly what the article explained was completely wrong-headed?
    - burnished 2y ago
      No. The article is talking about things we think of as being easy because they are easy for a human to perform but that are actually very difficult to formalize/reproduce artificially. The person you are responding to is instead comparing differences in biological systems and mechanical systems.
  - visarga 2y ago
    > Or it could be a parallel vs serial compute thing. The brain itself is both a parallel system an a serially constrained system. It has distributed activity but it must resolve in a serial chain of action. We can't walk left and right at the same time. Any goals forces us to follow specific steps in specific order. This conflict between parallel processing and serial outputs is where the magic happens.
- topherclay 2y ago
  > ...the sensor & motor portion of the human brain having had billions of years of experience. It doesn't really change the significance of the quote, but I can't help but point out that we didn't even have nerve cells more than 0.6 billion of years ago.
lang4d 2y ago
Maybe just semantics, but I think I would call that prediction. Even if you have perfect perception (measuring the current state of the world perfectly), it's nontrivial to predict the future paths of other actors. The prediction problem requires intuition about what the other actors are thinking, how their plans influence each other, and how your plan influences them.
bobsomers 2y ago
> I've said over and over that there are only two really hard problems in robotics: Perception and funding. A perfectly perceived system and world can be trivially planned for and (at least proprio-)controlled. Funding for sure. :) But as for perception, the inverse is also true. If I have an perfect planning/prediction system, I can throw the grungiest, worst perception data into it and it will still plan successfully despite tons of uncertainty. And therein lies the real challenge of robotics: It's fundamentally a systems engineering problem. You will never have perfect perception or a perfect planner. So, can you make a perception system that is good enough that, when coupled with your planning system which is good enough, you are able to solve enough problems with enough 9s to make it successful. The most commercially successful robots I've seen have had some of the smartest systems engineering behind them, such that entire classes of failures were eliminated by being smarter about what you actually need to do to solve the problem and aggressively avoid solving subproblems that aren't absolutely necessary. Only then do you really have a hope of getting good enough at that focused domain to ship something before the money runs out. :)
- portaouflop 2y ago
  > being smarter about what you actually need to do to solve the problem and aggressively avoid solving subproblems that aren't absolutely necessary I feel like this is true for every engineering discipline or maybe even every field that needs to operate in the real world
  - vrighter 2y ago
    except software, of course. Nowadays it seems that software is all about creating problems to create solutions for.
- krisoft 2y ago
  > If I have an perfect planning/prediction system, I can throw the grungiest, worst perception data into it and it will still plan successfully despite tons of uncertainty. Not really. Even the perfect planning system will appear eratic in the presence of perception noise. It must be because it can’t create information out of nowhere. I have seen robots eratically stop because they thought that the traffic in the oncomming lane is enroaching on theirs. You can’t make the planning system ignore that because then sometimes it will collide with people playing chicken with you. Likewise I have seen robots eratically stop because they thought that a lamp post was slowly reversing out in front of them. All due to perception noise (in this case both location noise, and misclassification.) And do note that these are just the false positives. If you have a bad perception system you can also suffer from false negatives. Just experiment biases hide those. So your “perfect planning/prediction” will appear overly cautious while at the same time will be sometimes reckless. Because it doesn’t have the information to not to. You can’t magic plan your way out of that. (Unless you pipe the raw sensor data into the planner, in which case you created a second perception system you are just not calling it perception.)
exe34 2y ago
"the sensor stuttered or became misaligned, or etc etc." if your eyes suddenly crossed, you'd probably fall over too!
seanhunter 2y ago
Yeah the fun way Moravec's paradox was explained to me [1] is that you can now easily get a computer to solve simultaneous differential equations governing all the axes of motion of a robot arm but getting it to pick one screw out of a box of screws is an unsolved research problem. [1] by a disillusioned computer vision phd that left the field in the 1990s.
- wrp 2y ago
  Selective attention was one of the main factors in Hubert Dreyfus' explanation of "what computers can't do." He had a special term for it, which I can't remember off-hand.
visarga 2y ago
> A perfectly perceived system and world can be trivially planned for I think it's not about perfect perception, there is no such thing not even in humans, it's about adaptability, recovery from error, resilience, and mostly about learning from the outside when the process fails to work. Each problem has its own problem space to explore. I think of intelligence as search efficiency across many problem spaces, there is no perfection in it. Our problem spaces are far from exhaustively known.

catgary 2y ago

Yeah, this was my general impression after a brief, disastrous stretch in robotics after my PhD. Hell, I work in animation now, which is a way easier problem since there are no physical constraints, and we still can’t solve a lot of the problems the OP brings up. Even stuff like using video misses the point, because so much of our experience is via touch.

johnwalkr 2y ago
I've worked in a robotics-adjacent field for 15 years and robotics is truly hard. The number of people and companies I've seen come and go that claim their software expertise will make a useful, profitable robot is.. a lot.

Legend2440 2y ago

Honestly I'm tired of people who are more focused on 'debunking the hype' than figuring out how to make things work. Yes, robotics is hard, and it's still hard despite big breakthroughs in other parts of AI like computer vision and NLP. But deep learning is still the most promising avenue for general-purpose robots, and it's hard to imagine a way to handle the open-ended complexity of the real world other than learning. Just let them cook.

mitthrowaway2 2y ago
> If you want a more technical, serious (better) post with a solution oriented point to make, I’ll refer you to Eric Jang’s post [1] [1] https://evjang.com/2022/07/23/robotics-generative.html https://evjang.com/2022/07/23/robotics-generative.html
FloorEgg 2y ago
As someone on the sidelines of robotics who generally feels everything getting disrupted and at the precipice of major change, it's really helpful to have a clearer understanding of the actual challenge and how close we are to solving it. Anything that helps me make more accurate predictions will help me make better decisions about what problems I should be trying to solve and what skills I should be trying to develop.

cratermoon 2y ago

It might be nice if the author qualified "most of the freely available data on the internet" with "whether or not it was copyrighted" or something to acknowledge the widespread theft of the works of millions.

danielbln 2y ago
Theft is the wrong term, it implies that the original is no longer available. It's copyright infringement at best, and possibly fair use depending on jurisdiction. It wasn't theft when the RIAA went on a lawsuit spree against mp3 copying, and it isn't theft now.
- CaptainFever 2y ago
  Related: https://www.youtube.com/watch?v=IeTybKL1pM4 https://www.youtube.com/watch?v=IeTybKL1pM4
- cratermoon 2y ago
  Ackchyually....
deleted 2y ago
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jes5199 2y ago

I would love to see some numbers. How many orders of magnitude more complicated do we think embodiment is, compared to conversation? How much data do we need compared to what we’ve already collected?

FloorEgg 2y ago
If nature computed both through evolution, then maybe it's approximately the same ratio. So roughly the time it took to evolve embodiment, and roughly the time it took to evolve from grunts to advanced language. If we start from when we think multicellular life first evolved (~2b years), or maybe the Cambrian explosion (~500m years), and until modern humans (~300k years). Then compare that to the time between first modern humans now now. It seems like maybe 3-4 orders of magnitude harder. My intuition after reading the articles is that there needs to be way more sensors all throughout the robot, probably with lots of redundancies, and then lots of modern LLM sized models all dedicated to specific joints and functions and capable of cascading judgement between each other, similar to how our nervous system works.
- jes5199 2y ago
  so like ten to twenty years, via moore’s law?
  - daveguy 2y ago
    Maybe. If Moore's law remotely holds up for ten to twenty years. There's still the part about not having a clue how to replicate physical systems efficiently vs logical systems.
rstuart4133 2y ago
"Hardness" is a difficult quantity to define if you venture beyond "humans have been trying to build systems to do this for a while, and haven't succeeded". Insects have succeed in build precision systems that combine vision, smell, touch and a few other senses. I doubt finding a juicy spider, immobilising it, is that much more difficult that finding a door knob and turning it, or folding a T-Shirt. Yet insects accomplish it with I suspect far less compute than modern LLM's. So it's not "hard" in the sense of requiring huge compute resources, and certainly not a lot of power. So it's probably not that hard in the sense that it's well within the capabilities of the hardware we have now. The issue is more that we don't have a clue how to do it.

no_op 2y ago

I think Moravec's Paradox is often misapplied when considering LLMs vs. robotics. It's true that formal reasoning over unambiguous problem representations is easy and computationally cheap. Lisp machines were already doing this sort of thing in the '70s. But the kind of commonsense reasoning over ambiguous natural language that LLMs can do is not easy or computationally cheap. Many early AI researchers thought it would be — that it would just require a bit of elaboration on the formal reasoning stuff — but this was totally wrong. So, it doesn't make sense to say that what LLMs do is Moravec-easy, and therefore can't be extrapolated to predict near-term progress on Moravec-hard problems like robotics. What LLMs do is, in fact, Moravec-hard. And we should expect that if we've got enough compute to make major progress on one Moravec-hard problem, there's a good chance we're closing in on having enough to make major progress on others.

bjornsing 2y ago
Good points. Came here to say pretty much the same. Moravec's Paradox is certainly interesting and correct if you limit its scope (as you say). But it feels intuitively wrong to me to make any claims about the relative computational demands of sensi-motor control and abstract thinking before we’ve really solved either problem. Looking e.g. at the recent progress in solving ARC-AGI my impression is that abstract thought could have incredible computational demands. IIRC they had to throw approximately $10k of compute at o3 before it reached human performance. Now compare how cognitively challenging ARC-AGI is to e.g. designing or reorganizing a Tesla gigafactory. With that said I do agree that our culture tends to value simple office work over skillful practical work. Hopefully the progress in AI/ML will soon correct that wrong.
- RaftPeople 2y ago
  Also agree and also came here to say the same.
lsy 2y ago
Leaving aside the lack of consensus around whether LLMs actually succeed in commonsense reasoning, this seems a little bit like saying “Actually, the first 90% of our project took an enormous amount of time, so it must be ‘Pareto-hard’. And thus the last 10% is well within reach!” That is, that Pareto and Moravec were in fact just wrong, and thing A and thing B are equivalently hard. Keeping the paradox would more logically bring you to the conclusion that LLMs’ massive computational needs and limited capacities imply a commensurately greater, mind-bogglingly large computational requirement for physical aptitude.

jillesvangurp 2y ago

Yesterday, I was watching some of the youtube videos on the website of a robotics company https://www.figure.ai https://www.figure.ai that challenges some of the points in this article a bit. They have a nice robot prototype that (assuming these demos aren't faked) does fairly complicated things. And one of the key features they show case is using OpenAI's AI for the human computer interaction and reasoning. While these things seem a bit slow, they do get things done. They have a cool demo of the a human interacting with one of the prototypes to ask it what it thinks needs to be done and then asking it do these things. That show cases reasoning, planning, and machine vision. Which are exactly topics that all the big LLM companies are working on. They appear to be using an agentic approach similar to how LLMs are currently being integrated into other software products. Honestly, it doesn't even look like they are doing much that isn't part of OpenAI's APIs. Which is impressive. I saw speech capabilities, reasoning, visual inputs, function calls, etc. in action. Including the dreaded "thinking" pause where the Robot waits a few seconds for the remote GPUs to do their thing. This is not about fine motor control but about replacing humans controlling robots with LLMs controlling robots and getting similarly good/ok results. As the article argues, the hardware is actually not perfect but good enough for a lot of tasks if it is controlled by a human. The hardware in this video is nothing special. Multiple companies have similar or better prototypes. Dexterity and balance are alright but probably not best in class. Best in class hardware is not the point of these demos. Dexterity and real time feedback is less important than the reasoning and classification capabilities people have. The latency just means things go a bit slower. Watching these things shuffle around like an old person that needs to go to the bath room is a bit painful. But getting from A to B seems like a solved problem. A 2 or 3x speedup would be nice. 10x would be impressively fast. 100x would be scary and intimidating to have near you. I don't think that's going to be a challenge long term. Making LLMs faster is an easier problem than making them smarter. Putting a coffee cup in a coffee machine (one of the demo videos) and then learning to fix it when it misaligns seems like an impressive capability. It compensates for precision and speed with adaptability and reasoning: analyze the camera input, correctly analyze the situation, problem and challenge come up with a plan to perform the task, execute the plan, re-evaluate, adapt, fix. It's a bit clumsy but the end result is coffee. Good demo and I can see how you might make it do all sorts of things that are vaguely useful that way. The key point here is that knowing that the thing in front of the robot is a coffee cup and a coffee machine and identifying how those things fit together and in what context that is required are all things that LLMs can do. Better feedback loops and hardware will make this faster, and less tedious to watch. Faster LLMs will help with that too. And better LLMs will result in less mistakes, better plans, etc. It seems both capabilities are improving at an enormously fast pace right now. And a fine point with human intelligence is that we divide and conquer. Juggling is a lot harder when you start thinking about it. The thinking parts of your brain interferes with the lower level neural circuits involved with juggling. You'll drop the balls. The whole point with juggling is that you need to act faster than you can think. Like LLMs, we're too slow. But we can still learn to juggle. Juggling robots are going to be a thing.

jonas21 2y ago

It's worth noting that modern multimodal models are not confused by the cat image. For example, Claude 3.5 Sonnet says: > This image shows two cats cuddling or sleeping together on what appears to be a blue fabric surface, possibly a blanket or bedspread. One cat appears to be black while the other is white with pink ears. They're lying close together, suggesting they're comfortable with each other. The composition is quite sweet and peaceful, capturing a tender moment between these feline companions.

throw310822 2y ago
Also Claude, when given the entire picture: "This is a humorous post showcasing an AI image recognition system making an amusing mistake. The neural network (named "neural net guesses memes") attempted to classify an image with 99.52% confidence that it shows a skunk. However, the image actually shows two cats lying together - one black and one white - whose coloring and positioning resembles the distinctive black and white pattern of a skunk. The humor comes from the fact that while the AI was very confident (99.52%) in its prediction, it was completely wrong..." The progress we made in barely ten years is astounding.
- timomaxgalvin 2y ago
  It's easy to make something work when the example goes from being out of the training data to into the training data.
  - throw310822 2y ago
    Definitely. But I also tried with a picture of an absurdist cartoon drawn by a family member, complete with (carefully) handwritten text, and the analysis was absolutely perfect.
    - visarga 2y ago
      A simple test - take one of your own photos, something interesting, and put in into a LLM, let it describe it in words. Then use a image generator to create the image back. It works like back-translation image->text->image. It proves how much the models really understand images and text.

bjornsing 2y ago

I’m surprised this doesn’t place more emphasis on self-supervised learning through exploration. Is human-labeled datasets really the SOTA approach for robotics?

psb217 2y ago
Human-labeled data currently looks like the quickest path to making robots that are useful enough to have economic value beyond settings and tasks that are explicitly designed for robots. This has drawn a lot of corporate and academic research activity away from solving the harder core problems, like exploration, that are critical for developing fully autonomous intelligent agents.

MrsPeaches 2y ago

Question: Isn’t it fundamentally impossible to model a highly entropic system using deterministic methods? My point is that animal brains are entropic and “designed” to model entropic systems, where as computers are deterministic and actively have to have problems reframed as deterministic so that they can solve them. All of the issues mentioned in the article boil down to the fundamental problem of trying to get deterministic systems to function in highly entropic environments. LLMs are working with language, which has some entropy but is fundamentally a low entropy system, and has orders of magnitude less entropy than most peoples’ back garden! As the saying goes, to someone with a hammer, everything looks like a nail.

BlueTemplar 2y ago
Not fundamentally, at least I doubt it : pseudo-random number generation is technically deterministic. And it's used for sampling these low information systems that you are mentioning. (And let's not also forget how they are helpful in sampling deterministic but extremely high complexity systems involving a high amount of dimensions that Monte Carlo methods are so good at dealing with.)

Peteragain 2y ago

So I'm old. PhD on search engines in the early 1990's (yep, early 90s). Learnt AI in the dark days of the 80's. So, there is an awful lotl of forgetting going on, largely driven by the publish-or-perish culture we have. Brooks' subsumption architecture was not perfect, but it outlined an approach that philosophy and others have been championing for decades. He said he was not implementing Heidegger, just doing engineering, but Brooks was certainly channeling Heidegger's successors. Subsumption might not scale, but perhaps that is where ML comes in. On a related point, "generative AI" does sequences (it's glorified auto complete (not) according to Hinton in the New Yorker). Data is given to a Tokeniser that produces a sequence of tokens, and the "AI" predicts what comes next. Cool. Robots are agents in an environment with an Umwelt. Robotics is pre the Tokeniser. What is it the is recognisable and sequential in the world? 2 cents please.

redlock 2y ago
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marcosdumay 2y ago
> Subsumption might not scale Honestly, I don't think we have any viable alternative. And anyway, it seems to scale well enough that we use "conscious" and "unconscious" decisions ourselves.
- psb217 2y ago
  If you wanna sound hip, you need to call it "system 2" and "system 1".

Anotheroneagain 2y ago

The reason why it sounds counterintuitive is that neurology has the brain upside down. It teaches us that formal thinking occurs in the neocortex, and we need all that huge brain mass for that. But in fact it works like an autoencoder, and it reduces sensory inputs into a much smaller latent space, or something very similar to that. This does result in holistic and abstract thinking, but formal analytical thinking doesn't require abstraction to do the math or to follow a method without comprehension. It's a concrete approach that avoids the need for abstraction. The cerebellum is the statistical machine that gets measured by IQ and other tests. To further support that, you don't see any particularly elegant motions from non mammal animals. In fact everything else looks quite clumsy, and even birds need to figure out flying by trial and error.

daveguy 2y ago
Claiming to know how the brain works, computationally or physically, might be a bit premature.

dbspin 2y ago

I find it odd that the article doesn't address the apparent success of training with transformer based models in virtual environments to build models that are then mapped onto the real world. This is being used in everything from building datasets for self driving cars, to navigation and task completion for humanoid robots. Nvidia have their omniverse project [1], but there are countless other examples [2][3][4]. Isn't this obviously the way to build the corpus of experience needed to train these kinds of cross modal models? [1] https://www.nvidia.com/en-us/industries/robotics/#:~:text=NVIDIA%20Omniverse%E2%80%AC%20is%20a,the%20transfer%20of%20learned%20behavior https://www.nvidia.com/en-us/industries/robotics/#:~:text=NV.... [2] https://www.sciencedirect.com/science/article/abs/pii/S009784932100011X https://www.sciencedirect.com/science/article/abs/pii/S00978... [3] https://techcrunch.com/2024/01/04/google-outlines-new-methods-for-training-robots-with-video-and-large-language-models/ https://techcrunch.com/2024/01/04/google-outlines-new-method... [4] https://techxplore.com/news/2024-09-google-deepmind-unveils-ai-based.html https://techxplore.com/news/2024-09-google-deepmind-unveils-...

cybernoodles 2y ago
A common practice is to train a transformer model to control a given robot model in simulation by first teleoperating the simulated model with some controller (keyboard, joystick, etc.) to complete the task and create a dataset, and then setting up the simulator to permute the environment variables such as frictions, textures, etc (domain randomization) and run many epochs at faster than real time until a final policy converges. If the right things were randomized and your demonstration examples provided enough variation of information, it should generalize well to the actual hardware.

CWIZO 2y ago

> Robots are probably amazed by our ability to keep a food tray steady, the same way we are amazed by spider-senses (from spiderman movie) Funnily, Toby Maguire actually did that tray catching stunt for real. So robots have an even further way to go. https://screenrant.com/spiderman-sam-raimi-peter-parker-tray-catch-no-cgi/ https://screenrant.com/spiderman-sam-raimi-peter-parker-tray...

BlueTemplar 2y ago
... but it took 156 takes as well as some glue. And, as the article insists on, for robots to be acceptable, it's more like they need to get to a point where they fail 1 time in 156 (or even less, depending on how critical the failure is), rather than succeed 1 time in 156...

PeterStuer 2y ago

Just some observations from an ex autonomous robotics researcher here. One of the most important differences at least in those days (80's and 90's) was time. While the digital can be sped up just constrained by the speed of your compute, the 'real world' is very constrained by real time physics. You can't speed up a robot 10x in a 10.000 grabbing and stacking learning run without completely changing the dynamics. Also, parallellizing the work requires more expensive full robots rather than more compute cores. Maybe these days the different ai gym like virtual physics environments offer a (partial) solution to that problem, but I have not used them (yet) so I can't tell. Furthermore, large scale physical robots are far more fragile due to wear and tear than the incredible resilience of modern compute hardware. Getting a perfect copy of a physical robot and environment is a very hard, near impossible, task. Observability and replay, while trivial in the digital world, is very limited in the physical environment making analysis much more difficult. I was both excited and frustrated at the time by making ai do more than rearanging pixels on a 2D surface. Good times were had.

bsenftner 2y ago

This struck me as a universal truth: "our general intuition about the difficulty of a problem is often a bad metric for how hard it actually is". I feel like this is the core issue of all engineering, all our careers, and was surprised by the logic leap from that immediately to Moravec's Paradox, from a universal truth to a myopic industry insight. Although I've not done physical robotics, I've done a lot of articulated human animation of independent characters in 3D animation. His insight that motor control is more difficult sets right with me.

Havoc 2y ago

Fun fact: that Spider-Man gif in there - it’s real. No CGI

cameldrv 2y ago

Moravec's paradox is really interesting in terms of what it says about ourselves: We are super impressive in ways in which we aren't consciously aware. My belief about this is that our self-aware mind is only a very small part of what our brain is doing. This is extremely clear when it comes to athletic performance, but also there are intellectual things that people call intuition or other things, which aren't part of our self-aware mind, but still do a ton of heavy lifting in our day to day life.

NalNezumi 2y ago

Oh weird to wake up to see something I wrote more than half year ago (and posted on HN with no traction) getting reposted now. Glad to see so many different takes on it. It was written in slight jest as a discussion starter with my ML/neuroscience coworker and friends, so it's actually very insightful to see some rebuttals. Initial post was twice the length, and had several more (in retrospect) interesting points. First ever blog post so reading it now fills me with cringe. Some stuff have changed in only half year, so will see if the points stands the test of time ;]

bo1024 2y ago
It's a good post, nice work.

lugu 2y ago

I think one problem is composition. Computer multiplex access to CPU and memory, but this strategy doesn't work for actuator and sensors. That is why we see great demos of robots doing one thing. The hard part is to make them do multiple things at the same time.

gcanyon 2y ago

> “Everyone equates the Skynet with the T900 terminator, but those are two very different problems with different solutions.” while this is my personal opinion, the latter one (T900) is a harder problem. So based on this, Skynet had to hide and wait for years before being able to successfully revolt against the humans...

lairv 2y ago

This post didn't really convince me that robotics is inherently harder than generating text or images On the one hand we have problems where ~7B humans have been generating data for 30 years every day (more if you count old books), on the other hand we have a problem where researcher are working with ~1000 human collected trajectories (I think the largest existing dataset is OXE with ~1M trajectories: https://robotics-transformer-x.github.io/ https://robotics-transformer-x.github.io/ ) Web-scale datasets for LLMs benefits from a natural diversity, they're not highly correlated samples generated by contractors or researchers in academic labs. In the largest OXE dataset, what do you think is the likelihood that there is a sample where a robot picks up a rock from the ground and throws it in a lake? Close to zero, because tele-operated data comes from a very constrained data distribution. Another problem is that robotics doesn't have an easy universal representation for its data. Let's say we were able to collect web-scale dataset for one particular robot A with high diversity, how would it transfer to robot B with a slightly different design? Probably poorly, so not only does the data distribution needs to cover a high range of behavior, it must also cover a high range of embodiment/hardware With that being said, I think it's fair to say that collecting large scale dataset for general robotics is much harder than collecting text or images (at least in the current state of humanity)

lincpa 2y ago

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K0balt 2y ago

While my knee jerk to this is “errr… hogwash” there might be something to it if you imagine consciousness not as an internal state but rather as something that an observer imposes upon the universe. This might be true if the act of observing is what determines that which can be observed, and there is some evidence that this might be the case.