Code as Policies: Language Model Programs for Embodied Control
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Let's unpack a paper titled Code as Policies: Language Model Programs for Embodied Control. What's the core idea here?

The core idea here is pretty darn cool. Imagine you've got these big language models that are great at writing code, the kind you use for autocomplete, right? Well, this paper, first published on Archive in September 2022, and the latest version, V4, was published in May 2023, says we can use those models to write code to control robots.

It's like teaching a robot to understand our commands by translating them into actions. That's interesting. Could you elaborate on how this translation works? Absolutely. So these language models can churn out code that essentially acts as a robot's policy, the rules it follows. This code can tap into the robot's perception, like object detection, and then hook up to its control systems.

This means the robot can process what it sees and then decide how to move or act accordingly. That's pretty neat. But how do we teach these language models to generate such code? Good question. The secret sauce is few shot prompting.

We basically show the model a bunch of examples where a natural language command, like "pick up the apple," is paired with the corresponding code that makes the robot do that. Then, when we give a new command, the model takes a crack at writing the right code. That sounds like a clever way to leverage these language models. What kind of tasks can they handle through this approach?

The cool thing is, these models aren't just limited to simple stuff. They can do some impressive things like spatial reasoning, where the robot understands concepts like left or right, and even generalize to new instructions that it hasn't seen before. It's like the robot is developing a kind of common sense understanding of how to behave.

This sounds like a big leap forward for robotics. What are some specific examples where this code as policies approach has been used? Oh, there have been some great demos. Imagine a robot arm drawing shapes on a whiteboard based on voice commands or even a mobile robot navigating a kitchen and putting things away. Those examples are quite impressive.

But every technology has its limitations, right? What are some of the challenges you see with this code as policies approach? You're absolutely right. There are definitely areas that need more work. One thing is that the model is somewhat limited by what the robot's perception system can detect. If the robot can't understand that a surface is bumpy, the model can't really act on that.

Also, complex or super long instructions can still be a bit tricky. I see. It seems like this approach is a promising avenue but still has some hurdles to overcome. Where do you think this research could lead in the future?

Quite optimistic about the potential here. As language models get even better and we refine the way we prompt them, we could see robots that can understand much more nuanced instructions. Imagine a future where you could simply tell your robot to tidy up the living room and it just gets it done.

That would be amazing. Thank you for discussing this interesting paper with me. It's been really enlightening to learn about this code as policies approach. Absolutely. It's been a pleasure diving into this with you. The idea of using language models to bridge the gap between human language and robot actions is incredibly exciting. I'm eager to see how this research continues to evolve. Thank you for discussing this.