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FAST: Efficient Action Tokenization for Vision-Language-Action Models - AI Robotics Tool

Overview

FAST is a universal action tokenizer that maps robot action sequences into dense, discrete tokens for training autoregressive vision-language-action models. The project provides a pre-trained tokenizer and tools to train a custom tokenizer on your own action data, published as a Hugging Face repository for robotics research and development.

Key Features

  • Universal action tokenizer for robot action sequences
  • Produces dense, discrete tokens for autoregressive models
  • Supports using a provided pre-trained tokenizer
  • Allows custom tokenizer training on user datasets
  • Hosted as a Hugging Face repository for easy access

Ideal Use Cases

  • Training autoregressive vision-language-action models
  • Tokenizing robotic action sequences for model inputs
  • Creating custom action vocabularies from collected data
  • Research in robotics, imitation, and language-conditioned control

Getting Started

  • Open the FAST repository on Hugging Face.
  • Clone or download the repository to your workspace.
  • Install the repository dependencies per instructions.
  • Load the provided pre-trained tokenizer for evaluation.
  • Prepare your action sequence dataset for custom training.
  • Run the repository's custom-training tools with your data.

Pricing

No pricing information disclosed for this repository or tools.

Key Information

  • Category: Robotics
  • Type: AI Robotics Tool
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