VLA - Vision Language Action Robotics Learning System

A vision-language-action integrated framework for robot learning and control

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Overview

VLA is an advanced robotics learning system that integrates visual perception, language understanding, and action control as three core modules. The system can understand natural language instructions and generate corresponding robot control commands based on visual input.

Key Features

• Multi-modal Learning - Integrate vision and language information
• Natural Language Instructions - Support Chinese and English commands
• Tactile Feedback Integration - Include tactile sensor data processing (optional)
• Modular Design - Easy to extend and customize
• Complete Demo - Provide out-of-the-box demonstration code
• Detailed Documentation - Chinese tutorials and technical documentation

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Project Structure

VLA/
├── models/                 # Core model architecture
│   ├── rdt/               # RDT (Robotics Diffusion Transformer) main model
│   ├── multimodal_encoder/ # Multi-modal encoder
│   └── rdt_runner.py      # Model inference entry point
│
├── train/                 # Training code
│   ├── train.py          # Main training loop
│   └── *.py              # Training utility functions
│
├── data/                  # Data processing module
│   ├── hdf5_vla_dataset.py       # HDF5 data loading
│   ├── unified_vla_dataset.py    # Unified data format
│   └── create_controller_dataset_episode.py # Data conversion
│
├── configs/               # Configuration files
│   ├── base.yaml         # Base configuration
│   ├── train_config.yaml # Training configuration
│   └── *.json            # Dataset and weight configuration
│
├── demo/                  # Demo and quick start
│   ├── run_demo.sh       # Interactive demo menu
│   ├── simple_visualize_data.py  # Data visualization
│   ├── interactive_demo.py       # Interactive inference testing
│   ├── START_HERE.md     # Quick start guide
│   └── 一頁總結.md       # Project overview
│
├── scripts/               # Utility scripts
├── main.py               # Direct training entry
├── finetune.sh          # Fine-tuning script
└── inference.sh         # Inference script

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Quick Start

Environment Setup

# Clone the repository
git clone https://github.com/your-username/VLA.git
cd VLA

# Create virtual environment (recommended with conda)
conda create -n vla python=3.9
conda activate vla

# Install dependencies
pip install -r requirements.txt

Running Demo (Recommended First)

cd demo

# Install demo dependencies (first time only)
bash install_demo_deps.sh

# Execute interactive menu (simplest way)
bash run_demo.sh

Demo menu includes:

• Data visualization (no training required)
• Interactive inference testing (quick model verification)
• Small-scale model training (optional)

Recommended Reading Order

1. demo/一頁總結.md - Quick overview in 3 minutes (highly recommended)
2. demo/START_HERE.md - Detailed introduction in 5 minutes
3. Run the demo code in practice
4. demo/quick_start_zh.md - Advanced setup (consult when needed)

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Main Training Commands

Train Full Model

python main.py \
    --config_path configs/train_config.yaml \
    --output_dir checkpoints/my_model \
    --load_from_hdf5

Fine-tune Pretrained Model

bash finetune.sh

Inference and Evaluation

bash inference.sh

See main.py and configuration files for detailed parameter descriptions

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Core Components Overview

Model Architecture

Component	Function	Description
RDT Model	Main model framework	Robotics Diffusion Transformer
Vision Encoder	Image encoding	Process visual input
Language Encoder	Text encoding	Understand natural language instructions
Action Decoder	Action generation	Output robot control signals

Data Format

Support multiple data formats:

• HDF5 - Efficient storage and loading
• TFRecord - TensorFlow standard format
• Unified format - Custom dataset support

Configurable Items

All main parameters are configured in configs/:

• Model size and architecture
• Training hyperparameters
• Dataset location and weights
• Inference parameters

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Functional Demonstration

Real-World Example

Robot autonomously picking mangoes based on visual and language instructions

Supported Functionality

Visual Input  →  Multi-modal Encoding  →  Action Prediction  →  Robot Execution
                      ↓
                Natural Language Instructions

Use Cases

• Object Grasping - Understand "pick up red cube" and execute
• Trajectory Imitation - Learn behavior from visual observation
• Transfer Learning - Quick fine-tuning on new tasks
• Multi-Robot - Support multiple robot platforms

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Project Highlights

Improvements over Baselines

• Multi-modal Fusion - Combine the dual advantages of vision and language
• Accuracy Improvement - [Specific numbers to be filled]
• Computational Efficiency - [Specific numbers to be filled]
• Usability - Out-of-the-box demo and documentation

Technical Stack

• Model Foundation - Diffusion Transformers
• Training Framework - PyTorch + Accelerate
• Multi-machine Training - DeepSpeed support
• Robot Control - [Supported platforms]

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System Requirements

Python >= 3.8
PyTorch >= 1.13
CUDA 11.8+ (GPU recommended)
RAM >= 16GB (32GB+ recommended for training)
GPU VRAM >= 24GB (for full training)

See requirements.txt for complete dependencies

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How to Use (For Resume)

For Interview and Project Presentations

1. Quick Demo (5 minutes)

   • Clone the repository
   • Run bash demo/run_demo.sh
   • Display visualization results

2. Code Review (In-depth technical discussion)

   • Core model in models/rdt/
   • Training logic in train/
   • Data processing in data/

3. Performance Showcase (Technical highlights)

   • Check evaluation metrics in test_results.txt
   • Discuss specific improvements and technical details

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File Navigation

Quick Reference

• Want a quick understanding? → demo/一頁總結.md
• Want to start immediately? → demo/START_HERE.md
• Want to learn in depth? → demo/quick_start_zh.md
• Want to modify code? → models/ and train/
• Want to adjust parameters? → configs/

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Technical Support

FAQ

Q: Can it run without GPU?
A: Yes, but slowly. It is recommended to use data visualization in the demo to understand the system.

Q: Can I use my own data?
A: Yes, see demo/prepare_own_dataset.py

Q: Which robots are supported?
A: Primarily Franka, with extensibility to support other platforms

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License

MIT License - See LICENSE for details

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Acknowledgments

This project is based on the following research and frameworks:

• Robotics Diffusion Transformer
• Vision-Language Multi-modal Learning
• VLA-Touch

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