AI is great at predicting text. Can it guide robots?

It seems like artificial intelligence is everywhere in our virtual lives. It's in our search results and our phones. But what happens when AI moves out of the chat and into the real world? NPR science editor and correspondent Geoff Brumfiel https://www.npr.org/people/279612138/geoff-brumfiel took a trip to the Intelligence through Robotic Interaction at Scale Lab at Stanford University to see how scientists are using AI to power robots and the large hurtles that exist for them to perform even simple tasks. (encore)Read Geoff's full story https://www.npr.org/2025/03/17/nx-s1-5323897/researchers-are-rushing-to-build-ai-powered-robots-but-will-they-work. Interested in more AI stories? Email us your ideas at [email protected] mailto:[email protected]. Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave http://plus.npr.org/shortwave. See pcm.adswizz.com https://pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences. NPR Privacy Policy https://www.npr.org/about-npr/179878450/privacy-policy

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• 0:00Speaker 00

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• 0:18Speaker 01

  You're listening to Short Wave from NPR.

• 0:23Regina Barber?

  Hey, Short Wavers. Regina Barber here. I don't know about you, but for me, recently, it seems like artificial intelligence is everywhere. It's in my search results, on everyone's phones, being pitched in my email, trying to read my emails. So I thought it would be a perfect time to revisit an episode we did last year with NPR science correspondent Jeff Brumfield. He noticed that AI isn't just showing up online anymore. It's creeping into reality, like at Tesla's big marketing event in 2024.

• 0:54Speaker 03

  Yep, AI was there.

• 0:57Speaker 04

  Speaking of robots.

• 1:00Speaker 03

  Tesla is obviously a car company, but Elon Musk, Tesla's CEO, made a big part of the event about a humanoid robot powered by AI and called Optimus.

• 1:11Speaker 04

  The software, the AI-powered computer, it all actually applies to a humanoid robot.

• 1:19Speaker 03

  And Google unveiled another humanoid robot that operates using AI.

• 1:23Speaker 04

  Hmm.

• 1:24Speaker 03

  We're bringing Gemini 2.0's intelligence to general-purpose robotic agents in the physical world.

• 1:30Regina Barber?

  OK, Jeff, but even before AI came along, people and companies have been making like big claims about robots.

• 1:36Speaker 03

  They have, they have. And the robots, as I'm sure you know, Gina, have always disappointed compared to the vision.

• 1:42Regina Barber?

  Yeah, that's true.

• 1:42Speaker 03

  And that's why I set out to understand the truth about AI and robotics.

• 1:47Regina Barber?

  The truth.

• 1:48Speaker 03

  And I think I kind of found it in a bowl of trail mix.

• 1:54Regina Barber?

  Today on the show, what happens when artificial intelligence moves out of the chat and into the real world?

• 2:00Speaker 03

  We're looking at how AI could maybe revolutionize robotics.

• 2:04Regina Barber?

  You're listening to Short Wave, the science podcast from NPR.

• 2:16Speaker 00

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• 3:27Regina Barber?

  OK, so Jeff, you were interested in finding out more about how AI works in robots. Where did you start?

• 3:32Speaker 03

  Well, I didn't go to Tesla or Google, but I did drive right by them on my way to Stanford University.

• 3:37Regina Barber?

  OK.

• 3:38Speaker 03

  And specifically, the IRIS Laboratory, which stands for Intelligence Through Robotic Interaction at Scale. I got a tour from a graduate student named Mu Jin Kim. Mu Jin works on a new kind of robot powered by AI, similar to the AI used in chatbots.

• 3:54Speaker 05

  It's one step in the direction of ChatGPT for robotics, but still a lot of work to do. OK, all right. Well, you want to show me how it does? Show me what it can do? For sure.

• 4:04Regina Barber?

  So Jeff, what did the robot look like?

• 4:07Speaker 03

  Well, this wasn't some humanoid robot that the big tech companies are rolling out. It's just a pair of mechanical arms with pinchers.

• 4:14Regina Barber?

  OK.

• 4:15Speaker 03

  But what made it interesting was that it's powered by an AI model called OpenVLA. So first, we should probably just say quickly, you know, a regular robot must be very, very carefully programmed. An engineer has to write it detailed instructions for every task you want it to perform.

• 4:31Regina Barber?

  Yeah, and AI is supposed to change that.

• 4:33Speaker 03

  Exactly, and that's what's going on here. This robot is powered by a teachable AI neural network. The neural network operates kind of how scientists think the human brain might work. Basically, there are these mathematical nodes in the network that have billions of connections to each other in a way similar to how neurons in the brain are connected together. And so when you go to program this sort of thing, it's simply about reinforcing the connections that matter between the nodes and weakening the other ones that don't. So in practice, this means Mu Jin can just teach OpenVLA a task by showing it.

• 5:08Speaker 05

  So basically, whatever task you want to do, you just keep doing it over and over, maybe like 50 times or 100 times.

• 5:15Speaker 03

  The robot's AI neural network becomes tuned to that task, and then it can do it by itself.

• 5:20Regina Barber?

  Yeah, it makes me think of this, like, smiling robot story we did, and that robot just watched, like, a lot of videos of people smiling. Then it learned how to do it.

• 5:28Speaker 03

  Yeah, it's exactly the same thing, except instead of just smiling, this robot's actually doing stuff.

• 5:34Regina Barber?

  Right.

• 5:34Speaker 03

  So to show me, Mu Jin brought out a tray of different kinds of trail mix, and I typed in what I wanted it to do.

• 5:40Speaker 05

  Okay, so scoop some green ones with the nuts into the bowl.

• 5:44Regina Barber?

  Oh.

• 5:45Speaker 05

  See what happens.

• 5:47Regina Barber?

  Okay, so Jeff, personally, I've been waiting for something like AI in robotics because you can teach it to do something, you can ask it to do something, to like make me an ice cream sundae or something, without like any fancy programming or special knowledge.

• 5:57Speaker 03

  That's exactly it, you know? And this really is the dream of the researcher who runs this laboratory. Her name is Chelsea Finn.

• 6:05Speaker 06

  So in the long term, we want to develop software that would allow the robots to operate intelligently in any situation.

• 6:12Speaker 03

  And by intelligently, she means the robot could understand a simple command, like scoop some green ones into a bowl, or make me a sundae, and then execute in the real world.

• 6:21Speaker 06

  Even just to do very basic things, like being able to make a sandwich, or being able to clean a kitchen, or being able to restock grocery store shelves.

• 6:29Speaker 03

  These are simple tasks that could help humans do their jobs or do tasks at home. Now, Chelsea also has co-founded a startup called Physical Intelligence. It recently demonstrated a mobile robot that could take laundry out of a dryer and fold it. Again, this robot was taught by humans, training its powerful AI program.

• 6:50Regina Barber?

  Okay, so ice cream sundaes, is that too advanced? Is folding an easier start?

• 6:55Speaker 03

  I mean, I'd actually argue, Gina, that folding is harder.

• 6:59Regina Barber?

  Okay.

• 6:59Speaker 03

  Let me show you a video.

• 7:01Regina Barber?

  Okay, it's going to the dryer, it's pulling stuff out, putting it in a basket. It has the concentration I have when I'm going to do laundry. It almost looks annoyed with folding, like I do. Oh, my God, it's doing really well, actually.

• 7:17Speaker 03

  Yes, it is, right? And this is a complicated task. It's got to pull these clothes out, it's got to figure out what they are.

• 7:24Regina Barber?

  It doesn't even have a head, but I'm giving it personality. It looks like it's like, oh, I just got to fold another one.

• 7:31Regina Barber?

  Okay, so is it really as simple as just teaching a robot what to do? Because if it was, wouldn't these robots be everywhere?

• 7:40Speaker 03

  Yeah, I mean, right? It looks cool on the video. The truth is that when you get out and these robots are trying to do these tasks over and over again, they get confused, they misunderstand, they make mistakes, and they just get stuck. So, you know, it might be able to fold laundry 90% of the time or 75% of the time, but the rest of the time, it's going to make a big mess that then a human has to get in there and clean up.

• 8:04Regina Barber?

  Got it, okay.

• 8:05Speaker 03

  I spoke to Ken Goldberg, a professor at the University of California at Berkeley, and he is pretty emphatic that AI-powered robots weren't here yet.

• 8:13Speaker 07

  Robots are not going to suddenly become the science fiction dream overnight.

• 8:18Regina Barber?

  Okay, so tell me why? Because AI chatbots have gotten way better, super fast. So why are these robots getting stuck?

• 8:25Speaker 03

  Okay, so it's true that AI has improved massively over the past couple of years, but that's because chatbots have a huge amount of data to learn from. They've taken basically the entire internet to train themselves how to write sentences and draw pictures. But Ken says-

• 8:41Speaker 07

  For robotics- There's nothing. We don't have anything to start with. Right? There's no examples online of robot commands being generated in response to robot inputs.

• 8:53Speaker 03

  And if robots really need as much training data as their virtual chatbot friends, then having humans teach them one task at a time is going to take a really long time.

• 9:03Speaker 07

  You know, at this current rate, we're going to take 100,000 years to get that much data.

• 9:07Regina Barber?

  What? Okay, that's so long. Like, are there any alternatives? There must be.

• 9:12Speaker 03

  Yeah, well, scientists are exploring them right now, and one might be to let the AI brain of the robot learn in a simulation. A researcher who's trying this is a guy named Poolkit Agrawal. He's at the Massachusetts Institute of Technology.

• 9:28Speaker 08

  The power of simulation is that we can collect, you know, very large amounts of data. For example, in three hours, you know, worth of simulation, we can collect 100 days worth of data.

• 9:39Speaker 03

  So this is a really promising approach for some things, but it's much more of a challenge for others. So for example, let's talk about walking. When you're just dealing with the earth and your body, the physics of walking around, it's actually kind of simple.

• 9:53Speaker 08

  When you're doing locomotion, you know, you're mostly on earth, you know, there's no amount of force you can apply which will make the earth move.

• 10:00Speaker 03

  And so the simulation can do that reasonably well, but if you want your robot to say, try and pick up a mug off a desk or something, that's a lot more complicated.

• 10:08Regina Barber?

  More forces.

• 10:09Speaker 08

  You know, if you apply the wrong forces, these objects can fly away very quickly.

• 10:14Speaker 03

  Basically, your robot will fling things across the room if it doesn't understand the weight and the size of what it's carrying. And there's more, you know, if your robot encounters anything that you haven't simulated 100% perfectly, then it won't know what to do. It'll just break.

• 10:29Regina Barber?

  Okay, so it sounds like these like simulations have limits and real world training is going to take like a while. I can begin to see why AI robots aren't going to like be here tomorrow.

• 10:39Speaker 03

  Exactly. And some researchers think there are even deeper problems, actually, with trying to put AI into robotics. One of them is Matthew Johnson-Roberson at Carnegie Mellon University in Pittsburgh.

• 10:49Speaker 09

  In my mind, the question is not, do we have enough data? It is more, what is the framing of the problem?

• 10:57Speaker 03

  So getting back to AI chatbots for a minute. Matt says for all their incredible skills, the task we're asking them to do is actually relatively simple. You know, you look at what a human user types and then try to predict the next words that user wants to see. Robots have so much more that they're going to have to do than just compose a sentence.

• 11:16Regina Barber?

  Right.

• 11:17Speaker 09

  Next best word prediction works really well. And it's a very simple problem because you're just predicting the next word. And it is not clear right now. I can take 20 hours of GoPro footage and then produce anything sensible with respect to how a robot moves around in the world.

• 11:31Speaker 03

  So in other words, the sci fi tasks that we want our robots to do are so complicated compared to sentence writing. No amount of data may be enough unless researchers can find the right way to teach the robots.

• 11:44Regina Barber?

  Or have the robots teach the robots.

• 11:46Speaker 03

  Yes, that's also an option. They can teach themselves.

• 11:49Regina Barber?

  OK, so Jeff, you've taken me from like optimist to pessimist. It's the, you know, the road I take every day. I'm starting to think that AI is like never going to work that well in robots. Or like it's going to be a really long time.

• 12:03Speaker 03

  You know, I'm sorry if I've like turned you into a pessimist here, Gina.

• 12:09Regina Barber?

  It happens.

• 12:10Speaker 03

  And I'm going to have to sort of whipshaw you back because AI is already finding its way into robotics in ways that are really interesting. So for example, Ken Goldberg has co founded a package sorting company. And just this year, they started using AI image recognition to pick the best points for their robots to grab the packages.

• 12:30Regina Barber?

  Oh, OK.

• 12:31Speaker 03

  Yeah, and it's working really well, he told me. And I think we're going to see a lot of that. AI being used for parts of the robotic problem, you know, walking or vision or whatever. It's going to make big progress. It just may not arrive everywhere all at once. And to really end on a high note here, let's get back to that Stanford lab. Remember, I asked it to grab some trail mix, right?

• 12:54Regina Barber?

  Yeah.

• 12:54Speaker 03

  So the robot correctly identified the right bin to Mu Jin Kim's relief.

• 13:00Speaker 05

  Usually that that spot right there where it identifies the object and goes to it, that's that's the part where we hold our breath.

• 13:06Speaker 03

  And then very, very slowly and kind of hesitantly, it reached out with its claw and picked up the scoop.

• 13:16Speaker 05

  It's doing it. Mujin, did I just program a robot? You did. Looks like it's working.

• 13:22Speaker 03

  And to my mind, it's incredible. Like, remember, nobody really programmed the robot exactly. This is all neural network learning how to move the claws and respond to the commands on its own. And to me, it's pretty wild that that works at all, and I think it's going to lead to some very cool developments.

• 13:39Regina Barber?

  I'm excited to hear more, Jeff. Thank you so much for bringing this reporting to us.

• 13:43Speaker 03

  Thank you very much.

• 13:45Regina Barber?

  We'll link Jeff's full story, which has robot videos, in our episode notes. This episode was produced by Burleigh McCoy, edited by our showrunner Rebecca Ramirez, and fact-checked by Tyler Jones. Jimmy Keely was the audio engineer.

• 13:59Speaker 03

  I'm Jeff Brumfiel.

• 14:00Regina Barber?

  I'm Regina Barber. Thank you for listening to Short Wave from NPR.

• 14:12Speaker 00

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