Octo: Generalist Robot Policy (Gen AI in Robotics)
Imagine a GPT for robots! That's what this paper is aiming for—a generalist robot that can perform a wide range of tasks. Right now, we have robots designed for specific, narrow tasks. This research is a step towards creating robots that can handle anything you throw at them
What’s a Generalist Robot Policy?
In simple terms, a policy is a neural network that decides what action a robot should take. It takes in observations (like text and images) and outputs control units.
Types of Control Units
1. Joint Control: The model specifies exactly what each joint should do (like the angle of an elbow).
2. End Effector: This is a fancy name for the gripper. The model outputs movement and rotation vectors for it.
The Process
1. Input: Task tokens (text), observation tokens (images), and readout tokens (a summary of task and observation tokens).
2. Output: Control units from the action head (a diffusion model).
Challenges
- Speed: Transformers are powerful but can be slow due to processing limitations. MLPs (Multi-Layer Perceptrons) can be faster but lack the generalization needed for different robots.
- Data: Currently, the available dataset is limited. However, with access to more data, we can expect these policies to become much more efficient.
In essence, this paper is pushing the boundaries in the field of robotics, aiming to create versatile robots capable of performing a wide array of tasks.
link to the paper:
https://arxiv.org/pdf/2405.12213
If you want me dive deeper,do let me know
