Vision-Language-Action Model (VLA) Research

By Rokawoo

ZhouLeRobotArm.mp4

Research combining self-supervised vision (DINOv2), vision-language alignment (CLIP), and action generation (π0) for robotics applications.

Foundation Models

DINOv2 (Meta AI)

Self-supervised vision transformer

• DINOv2: Learning Robust Visual Features without Supervision
• Emerging Properties in Self-Supervised Vision Transformers
• Project Page
• Meta AI Blog

CLIP (OpenAI)

Contrastive Language-Image Pre-training

• Learning Transferable Visual Models From Natural Language Supervision

π0 Models

Vision-Language-Action models

• π0: A Vision-Language-Action Flow Model
• π0.5: Enhanced Vision-Language-Action Model

VLA Caption Generator

Generate hierarchical, spatially-aware captions for training Vision-Language-Action models.

Environment Setup

# Create conda environment with Python 3.10
conda create -n vla_caption python=3.10 -y
conda activate vla_caption

# Install PyTorch (choose one)
# For CUDA 11.8:
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
# For CPU only:
conda install pytorch torchvision torchaudio cpuonly -c pytorch

# Install dependencies
pip install transformers==4.36.2 pillow==10.2.0 numpy==1.24.3 accelerate==0.25.0 sentencepiece==0.1.99 transformers accelerate hf_xet timm

Usage

from vla_caption import VLACaptionGenerator

# Initialize
generator = VLACaptionGenerator()

# Generate caption
caption = generator.generate_caption("image.jpg", "kitchen cleaning")

# Get training format
data = caption.to_training_format()

Output Format

{
  "high_level": "clean the kitchen",
  "semantic": "pick up the plate", 
  "low_level": "move gripper to plate at position (0.35, 0.62)",
  "grounding": {
    "plate": [0.25, 0.55, 0.45, 0.70],
    "cup": [0.60, 0.50, 0.75, 0.65]
  }
}

Requirements

• Python 3.10
• 8GB+ GPU VRAM (recommended) or 16GB RAM (CPU)
• 6GB disk space for model cache