This repository provides a PyTorch implementation of RT-1 (Robotics Transformer), a model designed for real-world robotic control at scale. It enables robots to learn complex tasks from multimodal data, including visual observations and natural language instructions, making it suitable for researchers and engineers working on embodied AI and robot learning.

How It Works

The implementation combines a MaxViT backbone for visual feature extraction with a Transformer architecture for sequence modeling. The MaxViT processes video frames, and its features are then fed into the RT-1 Transformer along with tokenized natural language instructions. This allows the model to condition its actions on both visual context and high-level task descriptions, facilitating efficient learning of diverse robotic skills.

Quick Start & Requirements

• Install via pip: pip install robotic-transformer-pytorch
• Requires PyTorch.
• Example usage and model instantiation are provided in the README.

Highlighted Details

• Implements RT-1, a key model from Google's Robotics team.
• Utilizes MaxViT as the vision backbone.
• Supports classifier-free guidance for conditional generation.
• Includes example code for both training and evaluation.

Maintenance & Community

• Sponsored by Stability.ai.
• Citations provided for RT-1, MaxViT, and Scalable Diffusion Models.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The repository is an implementation of a research paper and may not include all features or optimizations of the original Google implementation. The lack of an explicit license requires careful consideration for commercial applications.