This groundbreaking technology is changing the game for embodied agents by allowing them to plan using large language models in just a few shots.

But what exactly does that mean? Well, let’s break it down. In traditional planning methods, an agent would need to have a detailed map of its environment and be able to reason about every possible action it could take. This can be time-consuming and resource-intensive for both the agent and the programmer.

But with LLM-Planner, all that changes! By using large language models (LLMs) like GPT-3 or BERT, agents can quickly learn to plan based on just a few examples. This means they don’t need to have perfect knowledge of their environment or be able to reason about every possible action instead, they can rely on the LLM to do that for them!

And here’s where it gets really cool: because LLMs are trained on massive amounts of text data, they can also help agents learn from past experiences and improve over time. This means that as an agent interacts with its environment, it will become better at planning and making decisions based on what it has learned in the past!

So how does this work in practice? Let’s say you have a robot that needs to navigate through a maze to find a specific object. Instead of having to program every possible path for the robot to take, you can simply provide it with a few examples (like “go left at the fork” or “turn right and then go straight”) and let LLM-Planner do the rest!

And here’s where things get really exciting: because LLMs are so powerful, they can also help agents learn from past experiences and improve over time. This means that as an agent interacts with its environment, it will become better at planning and making decisions based on what it has learned in the past!

So if you’re ready to take your embodied agents to the next level, give LLM-Planner a try today! And who knows maybe one day we’ll have robots that can navigate through mazes like pros without any programming at all!