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Robotic Transformer Pytorch

Robotic Transformer Pytorch is an open-source Python and PyTorch implementation of RT-1 that takes video frames and natural-language instructions and produces binned robot-action logits. Its example uses a MaxViT visual component and RT1 model, with cross-attention conditioning and classifier-free guidance.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Robotic Transformer Pytorch — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code-level starting point for experimenting with RT-1-style action prediction from video and language, rather than a documented robot-control deployment workflow.

Who it is for

  • Python and PyTorch developers exploring robotics policy models.
  • Researchers and prototypers working on vision-language-conditioned action prediction.

Documented uses

  • Prototype a model that maps video observations and instructions to action predictions.
  • Explore RT1 model configuration with a MaxViT visual component.
  • Compare classifier-free guidance settings during evaluation.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the MIT license. The README documents installation with pip.

  • The model consumes video and text instructions and returns logits, not documented direct robot commands.
  • The README provides model construction and example inference, but not a training recipe; it notes evaluation after training.
  • The example explicitly supports classifier-free guidance through cond_scale.
  • The implementation is presented as Python/PyTorch code rather than a hosted application.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the library and inspect its model example

Set up the package and follow the documented construction and inference example to obtain action logits.

Before you start

  • Python environment with PyTorch available.
  • The robotic-transformer-pytorch package, installed with pip.

Steps

  1. Install the package with the command below.

    Documented command
    pip install robotic-transformer-pytorch

  2. Import MaxViT and RT1 from robotic_transformer_pytorch.

  3. Construct a MaxViT visual component and pass it to an RT1 model, as shown in the README.

  4. Pass video tensors and corresponding natural-language instructions to the model; the example returns logits shaped by batch, frames, actions and bins.

Expected outcome

A configured RT1 model produces binned action logits for supplied video and instructions.

Guide limitations

  • The example uses randomly generated video tensors and does not document a complete training procedure.
  • The README describes evaluation after training; it does not document connecting predictions to a physical robot.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
lucidrains/robotic-transformer-pytorch · Official homepage title · retained in its source language
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