[HN Gopher] OpenAI disbands its robotics research team
___________________________________________________________________
OpenAI disbands its robotics research team
Author : morty_s
Score : 76 points
Date : 2021-07-16 21:03 UTC (1 days ago)
(HTM) web link (venturebeat.com)
(TXT) w3m dump (venturebeat.com)
| Jack000 wrote:
| Makes sense I guess, integrating robot hardware requires an
| entirely different set of skills to ML research and has a much
| slower dev cycle.
|
| I think OpenAI has progressively narrowed down its core
| competency - for a company like 3M it would be something like
| "applying coatings to substrates", and for OpenAI it's more like
| "applying transformers to different domains".
|
| It seems like most of their high-impact stuff is basically a big
| transformer: GPT-x, copilot, image gpt, DALL-E, CLIP, jukebox,
| musenet
|
| their RL and gan/diffusion stuff bucks the trend, but I'm sure
| we'll see transformers show up in those domains as well.
| varelse wrote:
| Fascinating in the wake of Fei Fei Li's lab publishing
| significant work on embodied intelligence...
|
| https://arxiv.org/abs/2102.02202
|
| Not to mention a bunch of relatively inexpensive reinforcement
| learning research relying on consumer knockoffs of Spot from
| Boston Dynamics...
|
| Really does seem like they are following the money and while
| there's nothing wrong with that it's also nothing like their
| original mission.
| [deleted]
| coolspot wrote:
| The team was probably replaced by GPT-4. No need for humans to
| slow down great mind.
| wly_cdgr wrote:
| This feels like a strong sign that AGI is quite close now
| amerine wrote:
| Why do you think that?
| wly_cdgr wrote:
| They smell the urgency in the air, and they're close enough
| to the center to get a good and accurate whiff
| abeppu wrote:
| How on earth would you know if a whiff was accurate, when
| we're talking about something which has never before been
| created?
|
| I think even if you have intuitions about an approach, and
| have promising results, if you're trying to arrive at
| something new, it's really hard to know how far away you
| are.
| wly_cdgr wrote:
| It's just a hunch, no need to get your boxers in a bunch
| fartcannon wrote:
| This is lunacy. The first country/company to replace human labour
| with general bipedal robots, will reap wealth beyond imagination.
| The short sitedness is astonishing, if you ask me.
|
| I genuinely believe how we as a society act once human labour is
| replaced is first aspect of the great filter.
| tejtm wrote:
| There are no mechanisms in place for the generated wealth to
| benefit the replaced people, the wealth will go mainly to
| vanishingly few persons self selected to be okay with gross
| economic inequality.
|
| We have been at this since at least the dawn of the industrial
| revolution and do not have it right yet. Backing off and taking
| it slow now to let some cultural adjustments happen is a
| responsible step.
|
| My cultural norms are repulsed by the thought of me not working
| as much as possible, it is how I expect my value to society to
| be gauged (and rewarded).
|
| This line of reasoning will be (is) obsolete and we need
| another in its place globally.
|
| I hope some may have better ideas of what these new cultural
| norms should look like than I with my too much traditional
| indoctrination.
|
| I only know what I will not have it look like; humanity as
| vassals of non corporeal entities or elites.
| joe_the_user wrote:
| _There are no mechanisms in place for the generated wealth to
| benefit the replaced people, the wealth will go mainly to
| vanishingly few persons self selected to be okay with gross
| economic inequality._
|
| That hasn't stopped the march of progress so far.
| Conveniently (or not), humanoid robots do not appear likely
| for the foreseeable future. But keep worrying, the problem
| you list are appearing in other fashions anyway.
| ragebol wrote:
| > replace human labour with general bipedal robots
|
| No need for bipeds, car factories employ dumb robot arms, no
| humans needed. Not very general purpose robots though.
|
| The first country/company to create robots that can be
| instructed similar to a humans to do any job will indeed have
| great benefits, but how long until that happens? Not within any
| amount of time that an investor wants to see. I'm unsure if I
| will ever see that in my life (counting on ~60 years to go
| still maybe?)
| TaylorAlexander wrote:
| One thing that struck me recently was that the famous
| imagenet competition that was won by a neural net took place
| in 2012. So we have made fantastic advances in ten years. But
| I'd still say at best robots like you describe are 20 years
| away, and that's a long time horizon for a small
| organization.
| ragebol wrote:
| Has robotics had such an 'ImageNet moment'? Nothing springs
| to mind, just slow advancement over decades.
|
| If suddenly robot manipulators could grasp any object,
| operate any knob/switch, tie knots, manipulate cloth, with
| the same manipulator, on first sight, that would be quite a
| feat.
|
| But then there's still task planning which is a very
| different topic. And ... and .... So much still to develop
| for generally useful robots.
| TaylorAlexander wrote:
| Not yet. I have a four wheel drive robot I designed with
| four 4k cameras feeding in to an Nvidia Jetson Xavier.
| [1]
|
| Just getting it to navigate itself using vision would
| mean building a complex system with a lot of pieces
| (beyond the most basic demo anyway). You need separate
| neural nets doing all kinds of different tasks and you
| need a massive training system for it all. You can see
| how much work Tesla has had to do to get a robot to
| safely drive on public roads. [2]
|
| From where I am sitting now, I think we are making good
| inroads on something like an "Imagenet moment" for
| robots. (Well, I should note that I am a robotics
| engineer but I mostly work on driver level software and
| hardware, not AI. Though I follow the research from the
| outside.)
|
| It seems like a combination of transformers plus scale
| plus cross domain reasoning like CLIP [3] could begin to
| build a system that could mimic humans. I guess as good
| as transformers are we still haven't solved how to get
| them to learn for themselves, and that's probably a hard
| requirement for really being useful in the real world.
| Good work in RL happening there though.
|
| Gosh, yeah, this is gonna take decades lol. Maybe we will
| have a spark that unites all this in one efficient
| system. Improving transformer efficiency and achieving
| big jumps in scale are a combo that will probably get
| interesting stuff solved. All the groundwork is a real
| slog.
|
| [1] https://reboot.love/t/new-cameras-on-rover/277
|
| [2] https://www.youtube.com/watch?v=hx7BXih7zx8
|
| [3] https://openai.com/blog/clip/
| brutus1213 wrote:
| I am a researcher on the AI/Systems side and I wanted to
| chime in. Transformers are amazing for language, and have
| broken all the SOTA is many areas (at the start of the
| year, some people may have wondered if CNNs are dead
| [they are not as I see it]). The issue with Transformer
| models is the insane amount of data they need. There is
| some amazing progress on using unsupervised methods to
| help, but that just saves you on data costs. You still
| need an insane about of GPU horsepower to train these
| things. I think this will be a bottleneck to progress.
| The average university researcher (unless from tier 1
| school with large funding/donors) are going to pretty
| much get locked out. That basically leaves the 5-6 key
| corporate labs to take things forward on the transformer
| front.
|
| RL, which I think this particular story is about, is an
| odd-duck. I have papers on this and I personally have
| mixed feelings. I am a very applications/solutions-
| oriented researcher and I am a bit skeptical about how
| pragmatic the state of the field is (e.g. reward function
| specification). The argument made by the OpenAI founder
| on RL not being amenable to taking advantage of large
| datasets is a pretty valid point.
|
| Finally, you raise interesting points on running multiple
| complex DNNs. Have you tried hooking things to ROS and
| using that as a scaffolding (I'm not a robotics guy ..
| just dabble in that as a hobby so curious what the
| solutions are). Google has something called MediaPipe,
| which is intriguing but maybe not what you need. I've
| seen some NVIDIA frameworks but they basically do pub-sub
| in a sub-optimal way. Curious what your thoughts are on
| what makes existing solutions insufficient (I feel they
| are too!)
| TaylorAlexander wrote:
| Great comment thank you.
|
| Yes unless the industry sees value in a step change in
| the scale on offer to regular devs, progress on massive
| nets will be slow.
|
| Hooking things together is pretty much my job. I have
| used ROS extensively in the past but now I just hook
| things together using python.
|
| But I consider what Tesla is doing to be pretty
| promising, and they are layering neural nets together
| where the output of three special purpose networks feed
| in to one big one etc. They call that a hydra net. No
| framework like ROS is required because each net was
| trained in situ with the other nets on the output of
| those nets, so I believe all compute logic is handled
| within the neural network processor (at some point they
| integrate standard logic too but a lot happens before
| that). Definitely watch some Karpathy talks on that.
|
| And currently I am simply not skilled enough to compose
| multiple networks like that. So I _could_ use multiple
| standalone networks, process them separately, and link
| them together using IPC of some kind, but it would be
| very slow compared to what 's possible. That's why I say
| we're "not there yet". Something like Tesla's system
| available as an open source project would be a boon, but
| the method is still very labor intensive compared to a
| self-learning system. It does have the advantage of being
| modular and testable though.
|
| I probably will hand compose a few networks (using IPC)
| eventually. I mean right now I am working on two networks
| - an RL trained trail following network trained in
| simulation on segmentation-like data (perhaps using
| Dreamer V2), and a semantic segmentation net that is
| trained on my hand labeled dataset with "trail/not-trail"
| segmentation. So far my segmentation net works okay. And
| a first step will actually be to hand-write an algorithm
| to go from segmentation data to steering. My simulation
| stuff is almost working. I built up a training
| environment using Godot video game engine and hacked the
| shared memory neural net training add on to accept image
| data, but when I run the sim in training on DreamerV2,
| something in the shared memory interface crashes and I
| have not resolved it. [1]
|
| But all of this is a hobby and I have a huge work project
| [2] I am managing myself that is important to me, so the
| self driving off road stuff has been on pause. But I
| don't stress about it too much because the longer I wait,
| the better my options get on the neural network side.
| Currently my off road rover is getting some mechanical
| repairs, but I do want to bring it back up soon.
|
| [1] https://github.com/lupoglaz/GodotAIGym/issues/15
|
| [2] https://community.twistedfields.com/t/a-closer-look-
| at-acorn...
| brutus1213 wrote:
| First off, amazing farm-bot project! I am looking forward
| to reading the details on your site.
|
| Thx for the pointers on Tesla. Had not seen the Hydranet
| stuff. There was a Karpathy talk about 2 weeks back at a
| CVPR workshop .. he revealed the scale of Tesla's current
| generation deep learning cluster [1]. It is insane!
| Despite being in industrial research, I don't foresee
| ever being able to touch a cluster like that.
|
| A lot of our current research involves end-to-end
| training (some complex stuff with transformers and other
| networks stitched together). There was a CVPR tutorial on
| autonomous driving [2], where they pretty much said
| autonomy 2.0 is all about end-to-end. I've spoken to a
| few people who actually do commercial autonomy, and they
| seemed more skeptical on whether end2end is the answer in
| the near-term.
|
| One idea we toy with is to use existing frozen
| architectures (OpenAI releases some and so do other big
| players) and do a small bit of fine-tuning.
|
| [1] https://www.youtube.com/watch?v=NSDTZQdo6H8 [2]
| https://www.self-driving-cars.org/
| toxik wrote:
| Imagine that there only needs to be ten people to "run the
| world". What is the population size going to be then? Ten? As
| large as possible? Somehow it seems that the way we're headed,
| it'll be ten plus some administrative overhead.
| kadoban wrote:
| The way we're headed it'll be billions in misery and dozens
| in luxury.
| Zababa wrote:
| > The first country/company to replace human labour with
| general bipedal robots, will reap wealth beyond imagination.
|
| Humans ARE genral bipedal robots. The price of these robots is
| determined by the minimum wage.
| nradov wrote:
| We are decades away from being able to build a general bipedal
| robot that can snake out a plugged toilet or dig a trench or
| nail shingles to a roof. It's just not a rational goal yet. Aim
| lower.
| TaylorAlexander wrote:
| This is correct. Right now our best and brightest can only
| build demos that fall apart the moment something is out of
| place. Humanoid or even partial humanoid (wheeled base)
| robots are far from ready for general purpose deployment.
| Animats wrote:
| And we're further away since nobody bought Schaft from
| Google, and Schaft was shut down. They had the best humanoid.
|
| But so many of the little problems have been solved.
| Batteries are much better. Radio data links are totally
| solved. Cameras are small and cheap. 3-phase brushless motors
| are small and somewhat. Power electronics for 3-phase
| brushless motors is cheap. 3D printing for making parts is
| cheap.
|
| I used to work on this stuff in the 1990s. All those things
| were problems back then. Way too much time spent on low-level
| mechanics.
|
| You can now get a good legged dog-type robot for US$12K, and
| a good robot arm for US$4K. This is progress.
| joe_the_user wrote:
| I basically agree.
|
| I'd just note that "decades away" means "an unforeseeable
| number of true advances away" - which could mean ten years or
| could mean centuries.
|
| And private companies can't throw money indefinitely at
| problems others have been trying to solve and failing at.
| They can it once and a while but that's it.
| throwaway_45 wrote:
| If robots are doing all the work how will people make money to
| buy the stuff the robots make? Is Jeff Bezos going to own the
| whole world or are we going to have another French revolution?
| TaylorAlexander wrote:
| We should really endeavor to build collectively owned
| institutions that can purchase and operate the robots (and
| physical space) we depend on.
|
| EDIT: Imagine the "credit unions" I mention in the following
| linked comment, but holding homes and manufacturing space to
| be used by members.
| https://news.ycombinator.com/item?id=27860696
| xnx wrote:
| Interesting contrast to another story today:
| https://ai.googleblog.com/2021/07/speeding-up-reinforcement-...
| ansk wrote:
| Is the prevailing opinion that progress in reinforcement learning
| is dependent on algorithmic advances, as opposed to simply
| scaling existing algorithms? If that is the case, I could see
| this decision as an acknowledgement that they are not well
| positioned to push the frontier of reinforcement learning - at
| least not compared to any other academic or industry lab. Where
| they have seen success, and the direction it seems they are
| consolidating their focus, is in scaling up existing algorithms
| with larger networks and larger datasets. Generative modeling and
| self supervised learning seem more amenable to this engineering-
| first approach, so it seems prudent for them to concentrate their
| efforts in these areas.
| abeppu wrote:
| I think the premise of your question actually points to the
| real problem. In RL, b/c your current policy and actions
| determine what data you see next, you can't really just "scale
| existing algorithms" in the sense of shoving more of the same
| data through them on more powerful processors. There's a
| sequential process of acting/observing/learning which is
| bottlenecked on your ability to act in your environment (ie
| through your robot). Off-policy learning exists, but scaling up
| the amount of data you process from a bad initial policy
| doesn't really lead anywhere good.
| andyxor wrote:
| Reinforcement learning itself is a dead-end on a road to AI.
| They seem to slowly starting to realize it, probably ahead of
| academia.
| TylerLives wrote:
| What's the alternative?
| nrmn wrote:
| Why do you believe this to be the case?
| andyxor wrote:
| In a nutshell it's too wasteful in energy spent and it
| doesn't even try to mimic natural cognition. As physicists
| say about theories hopelessly detached from reality - "it's
| not even wrong".
|
| The achievements of RL are so dramatically oversold that it
| can probably be called the new snake oil.
| vladTheInhaler wrote:
| I'm going to need you to unpack that a bit. Isn't
| interacting with an environment and observing the result
| exactly what natural cognition does? What area of machine
| learning do you feel is closer to how natural cognition
| works?
| kirill5pol wrote:
| Maybe true if you consider policy gradient methods and Q
| learning the only things that exist in RL... it's a pretty
| wide field that encompasses a lot more than the stuff OpenAI
| puts out.
| nrmn wrote:
| Yes, it feels like we have squeezed most of the performance out
| of current algorithms and architectures. OpenAI and deepmind
| have thrown tremendous compute against the problem with little
| overall progress (overall, alpha go is special). There was a
| big improvement in performance by bringing in function
| approximators in the form of deep networks. Which as you said
| can scale upwards nicely with more data and compute. In my
| opinion as an academic in the deep RL, it feels like we are
| missing some fundamental pieces to get another leap forward. I
| am uncertain what exactly the solution is but any improvement
| in areas like sample efficiency, stability, or task transfer
| could be quite significant. Personally I'm quite excited about
| the vein of learning to learn.
| an_opabinia wrote:
| > alpha go is special
|
| The VC community is in denial about how much Go resembled a
| problem purpose built to be solved by deep neural networks.
| dougSF70 wrote:
| Designing robots to pick fruit and make coffee / pizzas cannot
| have a positive ROI until labor laws make the bsuiness-case for
| them. Majority of use cases where we can use robots for
| activities humans cannot perform (fast spot welding on production
| line, moving nuclear fuel rods, etc) have been solved. It is
| smart to focus on language and information processing, given that
| we are producing so much more of it, everyday.
| cweill wrote:
| I think the comments are confounding shutting down the robotics
| research team with eliminating all RL research. Most robotics
| teams don't use data-hungry RL algorithms because the cost of
| interacting with the environment is so expensive. And even if the
| team has a simulator that can approximate the real world to
| produce infinite data, there is still the issue of the
| "simulator-gap" with the real world.
|
| I don't work for openAI but I would guess they are going to keep
| working on RL (e.g hide and seek, gym, DoTA style Research) to
| push the algorithmic SoTA. But translating that into a physical
| robot interacting with the physical world is extremely difficult
| and a ways away.
| samstave wrote:
| Curious idea:
|
| With the mentioning that they can shift their focus to domains
| with extensive data that they can build models of action with
| etc... Why not try the following (If easily possible)
|
| ---
|
| Take all the objects on the various 3D warehouses (thingiverse,
| and all the other 3d modeling repos out there) -- and have a
| system whereby an OpenAI 'Robotics' platform can virtually learn
| to manipulate and control a 3D model
| (solidworks/blender/whatever) and learn how to operate it.
|
| It would be amazing to have an AI robotics platform where you
| feed it various 3D files of real/planned/designed machines, and
| have it understand the actual constituancy of the components
| involved, then learn its degrees of motion limits, or servo
| inputs etc... and then learn to drive the device.
|
| Then, give it various other machines which share component types,
| built into any multitude of devices - and have it eval the model
| for familiar gears, worm-screws, servos, motors, etc... and have
| it figure out how to output the controller code to run an actual
| physically built out device.
|
| Let it go through thousands of 3D models of things and build a
| library of common code that can be used to run those components
| when found in any design....
|
| Then you couple that code with Copilot and allow for people to
| have a codebase for controlling such based on what OpenAI has
| already learned....
|
| As Copilot is already built using a partnership with OpenAI...
| marcinzm wrote:
| I suspect it's because at a certain point detailed physics
| matters and simulating things well enough is really hard. A
| robotic arm might flex just a bit, a gear may not mesh quite
| correctly, signals may take just a bit longer to get somewhere,
| a grip might slip, a plastic object might break from too much
| force, etc, etc.
| robotresearcher wrote:
| Sounds like a perfect domain to explore robust methods that
| can't overfit to silly details.
| verall wrote:
| NVIDIA Isaac sounds very close to what you're describing.
| adenozine wrote:
| I'm sure the overhead and upkeep of a robotics lab far outweighs
| that of a computer lab for software research.
|
| Are there any Open* organizations for robotics that could perhaps
| fill the void here? I think robotics is really important and I
| think the software is a big deal also, but it's important that
| actual physical trials of these AIs are pursued. I would think
| that seeing something in real space like that offers an
| unparalleled insight for expert observers.
|
| I remember the first time I ever orchestrated a DB failover
| routine, my boss took me into the server room when it was
| scheduled on the testing cluster. Hearing all the machines spin
| up and the hard drives start humming, that was a powerful and
| visceral moment for me and really crystallized what seemed like
| importance about my job.
| spiritplumber wrote:
| www.robots-everywhere.com we have a bunch of free stuff hereif
| it helps any
| minimaxir wrote:
| The cynical-but-likely-accurate take is that researching language
| modeling has a higher ROI and lower risk than researching
| robotics.
| madisonmay wrote:
| Wojciech stated this pretty explicitly on his Gradient Dissent
| podcast a few months back.
| texasbigdata wrote:
| After a bit of Googling are you referring to Wojciech, the
| head of YouTube?
| ingenieros wrote:
| https://open.spotify.com/episode/0f9Ht2vtdCYuHvKjMGf0al?si=
| K...
| kirill5pol wrote:
| http://wojzaremba.com/
| Animats wrote:
| Yes.
|
| Also, regular ML researchers sit at tables with laptops.
| Robotics people need electronics labs and electronics
| technicians, machine shops and machinists, test tracks and test
| track staff...
|
| If you have to build stuff, and you're not in a place that
| builds stuff on a regular basis, it takes way too long to get
| stuff built.
| [deleted]
| amelius wrote:
| Order picking in e-commerce warehouses seems a potentially
| profitable market.
| johnmoberg wrote:
| Definitely! Pieter Abbeel (who was working with OpenAI at
| some point) and others realized this and founded
| https://covariant.ai/.
| [deleted]
| high_derivative wrote:
| I dont think this is cynical and I don't think it's a bad
| thing. OpenAI is not a huge org. The truth in 2021 is that not
| only is robotics 'just not there yet' in terms of being a
| useful vehicle for general intelligence research (obviously
| robotics research itself is still valuable), there is also
| nothing really pointing at this going to be the case in the
| next 5-10 years.
|
| Given that, unless they want to commercialise fruit picking or
| warehouse robots, it seems sensible.
| BigBubbleButt wrote:
| > Given that, unless they want to commercialise fruit picking
| or warehouse robots, it seems sensible.
|
| How successful do you think attempts to monetize this will
| be? Apart from Kiva at Amazon, I'm not even sure most shelf-
| moving robots are profitable enterprises (GreyOrange,
| Berkshire Grey, etcetera). I'm very skeptical of more general
| purpose warehouse robots such as you see from Covariance,
| Fetch, etcetera. I don't really know too much about fruit-
| picking other than grokking how hard it would be and how
| little it would pay.
|
| To be clear, I'm not saying these companies make no money or
| have no customers. But it's not clear to me that any of them
| are profitable or likely will be soon, and robots are very
| expensive. I'm happy to learn why I'm wrong and these
| companies/technologies are further ahead than I realize.
| zitterbewegung wrote:
| I was wondering why OpenAI's gym was archived on GitHub this
| pivot seems more sense.
| Syntonicles wrote:
| Can you explain what that means? I'm familiar with OpenAI
| Gym, I've been away from Github for a long time.
| the-dude wrote:
| Read only
| jablongo wrote:
| GYM is not exclusively for robotics - it's for reinforcement
| learning in simulated environments, which I'm sure they will
| keep doing. Also it looks like it is still being maintained,
| so not really sure what you mean.
| fxtentacle wrote:
| My prediction is that dropping the real-world interactions will
| severely slow down their progress in other areas. But then
| again, I'm super biased because my current work is to make AI
| training easier by building specialized hardware.
|
| Reinforcement learning can work quite well if you produce the
| hardware, so that your simulation model perfectly matches the
| real-world deployment system. On the other hand, training
| purely on virtual data has never really worked for us because
| the real world is always messier/dirtier than even your most
| realistic CGI simulations. And nobody wants an AI that cannot
| deal with everyday stuff like fog, water, shiny floors, rain,
| and dust.
|
| In my opinion, most recent AI breakthroughs have come from
| restating the problem in a way that you can brute-force it with
| ever-increasing compute power and ever-larger data sets. "end
| to end trainable" is the magic keyword here. That means the
| keys to the future are in better data set creation. And the
| cheapest way to collect lots of data about how the world works
| is to send a robot and let it play, just like how kids learn.
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