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

Overview

FAST (Efficient Action Tokenization) provides a universal action tokenizer (FAST+) that maps robot action sequences into dense, discrete tokens for training autoregressive vision-language-action models. The Hugging Face repository includes a pre-trained tokenizer and tools to train a custom tokenizer on your own action data.

Key Features

  • Universal action tokenizer (FAST+) for robot action sequences
  • Maps sequences of robot actions into dense, discrete tokens
  • Designed for autoregressive vision-language-action model training
  • Supports usage of a pre-trained tokenizer
  • Enables custom tokenizer training on user datasets
  • Distributed as a Hugging Face repository

Ideal Use Cases

  • Tokenizing robot action sequences for model training
  • Training autoregressive vision-language-action models
  • Building dataset-specific action tokenizers
  • Research on language-conditioned robotic behaviors
  • Preprocessing action data for sequence models

Getting Started

  • Visit the repository's Hugging Face page
  • Read the README and included examples
  • Install the repository's required dependencies
  • Try the pre-trained tokenizer on sample action sequences
  • Prepare your action dataset for custom training
  • Train a custom tokenizer and integrate tokens into training

Pricing

No pricing information is provided in the repository metadata.

Key Information

  • Category: Robotics
  • Type: AI Robotics Tool
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