Well, let me introduce you to Instruct2Act, a new system developed by researchers at OpenGVLab.

Instruct2Act is all about mapping multi-modality instructions to robotic actions using LLMs. Basically, it allows us to give our robot friends commands in natural language and have them carry out those tasks with ease. And let me tell you, this system is a game changer!

Here’s how it works: first, we feed the LLM some text instructions (like “open the door” or “turn on the lights”). The LLM then processes that information and generates an action plan for our robot friend to follow. This action plan includes things like which joints to move, at what speed, and in what direction.

But wait there’s more! Instruct2Act also takes into account other modalities (like images or videos) when generating its action plans. So if we give it instructions that include visual information (like “go to the red door”), it can use that information to help guide our robot friend towards its destination.

And here’s the best part Instruct2Act is incredibly accurate and efficient! According to the researchers at OpenGVLab, their system was able to achieve a success rate of over 95% in simulated environments (which is pretty ***** impressive if you ask me). And because it uses LLMs, it can handle complex instructions with ease no more struggling to understand simple commands like “turn left” or “go forward”.

So what are some practical applications for Instruct2Act? Well, imagine being able to give your robot friend a list of tasks to complete while you’re out running errands. Instead of having to manually program each task into the robot (which can be time-consuming and frustrating), you could simply provide it with instructions in natural language. And because Instruct2Act is so accurate, you can trust that your robot friend will carry out those tasks without any issues.

But wait there’s more! Because Instruct2Act uses LLMs to generate its action plans, it can also learn from past experiences and improve over time. So if our robot friend encounters a new task (like “open the window”), it can use that information to help guide future actions and make them even more efficient.