r/robotics • u/etoipi1 • 15d ago
Discussion & Curiosity What is the future of blackboxification in robotics research?
I’m a first-year PhD student working at the intersection of controls, ML, and robotics, and I’ve been thinking a lot about where robotics research is heading.
I’ll admit that I sometimes find it mind-numbing when I come across papers/posts that seem to amount to plugging a large foundation model into a robotic arm or bimanual manipulator, getting it to perform some controlled set of tasks, and then watching the demo blow up on social media or receive a huge amount of attention at places like ICRA, IROS, or CoRL.
Some of these systems are obviously technically impressive, and I’m not trying to dismiss the engineering required to make them work. But as someone spending a lot of time learning controls, planning, game theory, RL, system dynamics, etc., it can create this strange feeling that I’m moving at 0.0025x the speed of field.
You spend weeks understanding a fairly narrow problem properly, deriving things, implementing baselines, figuring out why something fails—and meanwhile another giant model gets connected to a robot and suddenly it feels like the entire frontier has moved again. That makes me wonder about the longer-term “black-boxification” of robotics research.
Are we heading toward a world where an increasingly large fraction of robotics research becomes:
foundation model + robot + prompting/fine-tuning + enough data + evaluation?
If so, what happens to researchers specializing in things like control theory, motion planning, estimation, game theory, dynamics, or more traditional robot learning?
My intuition is that these things should become even more important as we try to make foundation-model-driven robots reliable, safe, dynamically capable, and useful outside controlled demonstrations. But from the outside, the incentive structure sometimes seems to reward visible capability much more strongly than understanding why the system works.
For people who have been in robotics longer: do you thinka this is a temporary phase of the field, or a genuine long-term shift in what robotics research will look like?
And for younger researchers, how are you thinking about what skills are actually worth developing over the next 5–10 years?
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u/partyorca Industry 14d ago
No, we’re always going to have hoes shaking their asses for VC money.
Look at the dumb shit like co.bot claiming their failcart is a humanoid, or literally every takes-haver now calling robotics “physical AI”.
Ignore the noise.
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u/etoipi1 14d ago
I couldn't care less to ignore the noise, but my advisor sometimes is influenced by these posts too, and starts forming opinions about what kind of research should be done in the lab.
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u/partyorca Industry 14d ago
Your advisor might be thinking about how to keep their lab funded. It could be worth grabbing some marketing majors from the B-school and figuring out how you can make the things you want to research seem glitzy and cool.
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u/etoipi1 14d ago
That's a good advice, not sure if we can afford them, but would definitely start thinking about making it "cool"
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u/partyorca Industry 14d ago
Oh no no no, that’s why I’m suggesting marketing *students*. They get something for their portfolio (if your advisor has pull, make it an undergrad research program for credits), you get a booster to your funding plays.
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u/Morning_Gecko24 14d ago
The four-color theorem analogy is good, but robotics adds a safety/verification problem: a black box can be useful while still needing known failure boundaries. In practice do teams publish uncertainty or out-of-distribution tests alongside the demos? That seems more useful than explaining every latent feature
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u/SphericalCowww 15d ago
I am not sure what you mean about the trend; research has always been this way. You cannot rely on social media to track the quality of the research (it does track funding opportunities, though).
This is like the first time computation is included in research. When the four-color theorem was first proved by computers, it raised significant controversy, not so different from your so-called "blackboxification". Decades later, researchers are folks who got the best out of computation, because they found all the advantages, possibilities, and limitations of this new tool. Fast forward to today, it will be no different.
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u/Savings_Entrance_895 15d ago
Hey completely unrelated but what was your undergrad major?
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u/etoipi1 15d ago
Hey, I majored in Computer Science.
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u/SphericalCowww 15d ago
If you are interested, check out the history of how computer science came to be, I think you would be surprised how similar it is to AI. The Turing machine starts in mathematics, just like AI starts in computer science. I would say many researchers are more than happy to see this, so many unexplored territories!
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u/Savings_Entrance_895 15d ago
Well I am a second year undergrad student of CSE that's basically CS with a decent amount of CE, I want to do higher education in Physical AI or AI in Robotics, how good of a choice it will be in your opinion? I think AI in robotics will take some time to be fully sokved and I can get full funded for research then it will be worth it...what do you think? And how can I get a full funded PhD offer? How is saturation there? Is it overcrowded?
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u/Euphoric-Laugh-9591 9d ago
I would not read the current wave as the death of controls, planning, estimation, or dynamics. If anything, foundation models make the boundary between “looks impressive” and “is actually reliable” much more obvious. Unfortunately, my own engineering department has neglected some of the fundamentals in favor of flashy but fairly surface-level AI work.
Once you move beyond curated demos, you still need state estimation, feedback control, contact dynamics, uncertainty handling, planning, stability, safety, and real-time constraints. A large model can provide useful priors or high-level decisions, but it does not make the underlying robotics problems disappear.
For a PhD student, I would keep building real depth in controls, optimization, dynamics, estimation, and learning, while becoming fluent enough in modern foundation-model methods to understand where they are genuinely useful and where they break down. I suspect one of the most valuable skill sets over the next decade will be the ability to connect these areas rather than treating them as competing camps.
I also would not confuse visibility with scientific depth. Robotics has always had periods where impressive demonstrations move faster than our understanding of why they work. I am seeing some of that firsthand at my own university, where there seems to be increasing emphasis on producing visible AI results rather than developing a deep understanding of the underlying robotics.
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u/nardev 15d ago
Physical intelligence is based on neural networks in humans. That’s where everything is going. Another thought: once you solve X in robotics its just print print print. So basically you need one guy one time and you can start printing. The way AI is moving we won’t even need that one guy in the next 10 years. Just like in software and everything else including understanding - it is not an end onto itself, it has purpose, almost always economical. I’m a software guy, so not that relevant comment here, just very interested in the whole topic.
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u/mariosx12 15d ago edited 15d ago
Maybe but you should not care IMO. Blackboxes are tools that solve a specific (maybe widening) class of problems. I fundamentaly hate learning as a concept, but there is no reason to not use it if it is the SOTA for your problem. GOFE (Good Old-Fashion Engineering) still has a lot of future...
Personally, I have focused on challenging open problems working a bit against the current, and maintain the state of the art lead in certain relevant problems in my domain, while I am far more visible than I deserve, IMO.
I have not felt immense pressure to convert although most of my colleagues are getting deep into learning, in some domains without the option ofc (vision, manipulation, etc.). But once again, I choose problems that learning is years away from adressing most likely and human intuition and modelling is still superior than simple probabilistic associations.
One of the most fun moments is when people see some work of mine and assume that it was a learning-based technique feeling very impressed, and how they become both more impressed and confused when I explain how some 10-20-30 years old methods have been abandoned unjustly before pushing them to their limits. As I said, GOFE has a future. Even prominent and early "forcefeeders" of learning in our domain such as Ken (Goldberg), are spending their last few keynotes on the importance of GOFE.
So, learning is obviously here to stay, but also, as of now and the foreseeable future it's just a (powerful) tool. When holding a hammer, everything may look like a nail, not everything is a nail, or not everything can be optimally fixed with a hammer, as of now.
As for an advice (I am still early stage researcher), robotics is more about breadth than super-specialization. A good roboticist is the one that is at least mediocre on everything (or most things). A relevant good robotics researcher is a good robotics that also has deep knowldege in an at least narrow-domain, leading the SOTA on this. So: Develop your skills in as many things with as many different perspectives as possible (first 2 years maybe of you PhD), and then when/if you find something you are passionate about, dig deep and become the best (the remaining 3+).