Fine-Tune Vision-Language-Action Models for Robotics

Vision-Language-Action (VLA) models are an important advancement in embodied AI. They bring together visual perception, language comprehension, and robotic control in a single model architecture that can interpret natural language commands and use them to complete physical tasks. This tutorial explains the full workflow for fine-tuning VLA models for individual robotic use cases.

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What Are Vision-Language-Action Models?

VLA models combine computer vision, natural language processing, and action execution. Computer vision allows the model to interpret images or videos, language processing enables it to understand and generate text, and action execution allows it to interact with environments, robots, or systems. As a result, these models can observe, reason, and act using both visual and textual input.

To see VLA models in practical use, you can watch the Google DeepMind Robotics Lab Tour with Hannah Fry. Well-known VLA model architectures include OpenVLA, RT-2 (Robotic Transformer 2), and PaLM-E.

Key Takeaways

VLA models connect vision, language, and action by translating camera images and natural language instructions directly into robot actions through an end-to-end model structure.
LoRA is useful for efficient fine-tuning because it trains only around 0.1-0.2% of the model parameters instead of updating the complete model. This makes it possible to train on a single GPU with 24GB of VRAM.

Prerequisites

Hardware Requirements

A GPU with at least 24GB of VRAM is required, such as an RTX 3090, RTX 4090, A100, or H100.
You also need robot hardware or a simulation environment.
Camera input is required for visual observations when running inference.
Robot hardware or a simulation environment is required for testing and execution.

Step 0: Set Up GPU Access and Jupyter Notebook

Prepare a GPU-enabled server or workstation and connect to it through SSH using your terminal.

In the terminal:

Console

# Core dependencies
pip install torch torchvision
pip install transformers accelerate
pip install datasets
pip install wandb  # for experiment tracking
pip install jupyter lab
jupyter lab

Step 1: Understand the VLA Architecture

The main components of a VLA model are the vision encoder, language encoder, fusion module, and action decoder.

The vision encoder processes camera images and turns them into visual embeddings. The language encoder converts text instructions into language embeddings. The fusion module combines the visual and textual information. The action decoder then predicts robot actions based on the fused representation.

Step 2: Prepare Your Dataset

Data Collection

Your dataset should contain episodes with the following elements:

Images: RGB camera observations for each timestep.
Language: Natural language descriptions of the tasks.
Actions: Robot action sequences, such as joint positions, velocities, or end-effector poses.
Metadata: Success labels, episode length, and similar information.

Data Format Example

JSON

{
    "episode_0": {
        "images": [img_0, img_1, ..., img_T],  # shape: (T, H, W, 3) where T is total frames
        "language": "pick up the red block",
        "actions": [act_0, act_1, ..., act_T],  # shape: (T, action_dim)
        "success": True
    }
}

Dataset Organization

Python

from datasets import Dataset, DatasetDict
import numpy as np

def create_vla_dataset(episodes):
    """Convert robot episodes to HuggingFace dataset format"""
    
    data = {
        "images": [],
        "language": [],
        "actions": [],
        "episode_id": []
    }
    
    for ep_id, episode in enumerate(episodes):
        for t in range(len(episode['images'])):
            data['images'].append(episode['images'][t])
            data['language'].append(episode['language'])
            data['actions'].append(episode['actions'][t])
            data['episode_id'].append(ep_id)
    
    return Dataset.from_dict(data)

# Split into train/val
dataset = create_vla_dataset(your_episodes)
dataset = dataset.train_test_split(test_size=0.1)

Step 3: Set Up the Base Model

Load a Pre-Trained VLA Model

Python

from transformers import AutoModel, AutoProcessor
import torch

# Load pre-trained VLA (example using OpenVLA)
model_name = "openvla/openvla-7b"
processor = AutoProcessor.from_pretrained(model_name)
model = AutoModel.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Freeze base model parameters (optional for LoRA)
for param in model.parameters():
    param.requires_grad = False

Adapt the Action Space

The action space of your robot will usually be different from the data used during pre-training:

Python

from torch import nn

class ActionHead(nn.Module):
    def __init__(self, hidden_dim, action_dim):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(hidden_dim, 512),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(512, action_dim)
        )
    
    def forward(self, x):
        return self.fc(x)

# Add custom action head
model.action_head = ActionHead(
    hidden_dim=model.config.hidden_size,
    action_dim=7  # e.g., 6-DOF arm + gripper
)

Step 4: Use LoRA for Efficient Fine-Tuning

Low-Rank Adaptation, or LoRA, makes fine-tuning more efficient by lowering the number of trainable parameters:

Python

from peft import LoraConfig, get_peft_model

# Configure LoRA
lora_config = LoraConfig(
    r=16,  # rank
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)

# Apply LoRA to model
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
#Expected output: trainable params: 8.3M || all params: 7B || trainable%: 0.12%

Step 5: Process and Augment the Data

Image Preprocessing

Python

from torchvision import transforms

image_transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
    transforms.RandomHorizontalFlip(p=0.5),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                        std=[0.229, 0.224, 0.225])
])

Action Normalization

Python

def normalize_actions(actions, stats):
    """Normalize actions to [-1, 1] range"""
    return (actions - stats['mean']) / (stats['std'] + 1e-8)

def denormalize_actions(normalized_actions, stats):
    """Convert back to original action space"""
    return normalized_actions * (stats['std'] + 1e-8) + stats['mean']

# Calculate statistics from your dataset
action_stats = {
    'mean': dataset['train']['actions'].mean(axis=0),
    'std': dataset['train']['actions'].std(axis=0)
}

DataLoader Setup

Python

from torch.utils.data import DataLoader

def collate_fn(batch):
    """Custom collate function for VLA data"""
    images = torch.stack([processor.image_processor(b['images']) 
                          for b in batch])
    text = processor.tokenizer([b['language'] for b in batch], 
                               padding=True, return_tensors="pt")
    actions = torch.tensor([b['actions'] for b in batch], 
                           dtype=torch.float32)
    
    return {
        'pixel_values': images,
        'input_ids': text['input_ids'],
        'attention_mask': text['attention_mask'],
        'actions': actions
    }

train_loader = DataLoader(
    dataset['train'],
    batch_size=32,
    shuffle=True,
    collate_fn=collate_fn,
    num_workers=4
)

Step 6: Build the Training Loop

Loss Function

Python

import torch.nn.functional as F

def vla_loss(predicted_actions, target_actions, reduction='mean'):
    """Action prediction loss (MSE for continuous actions)"""
    mse_loss = F.mse_loss(predicted_actions, target_actions, reduction=reduction)
    return mse_loss

# Alternative: Action chunking for temporal coherence
def chunked_action_loss(pred_chunks, target_chunks, chunk_size=10):
    """Predict multiple future actions at once"""
    loss = 0
    for i in range(chunk_size):
        loss += F.mse_loss(pred_chunks[:, i], target_chunks[:, i])
    return loss / chunk_size

Training Script

Python

from accelerate import Accelerator
from tqdm import tqdm
import wandb

# Initialize accelerator for distributed training
accelerator = Accelerator(mixed_precision='bf16')

# Setup
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0.01)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10000)

# Prepare for distributed training
model, optimizer, train_loader = accelerator.prepare(
    model, optimizer, train_loader
)

# Initialize wandb
wandb.init(project="vla-finetuning", config={
    "learning_rate": 1e-4,
    "batch_size": 32,
    "epochs": 10
})

# Training loop
global_step = 0
for epoch in range(10):
    model.train()
    epoch_loss = 0
    
    for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}"):
        # Forward pass
        outputs = model(
            pixel_values=batch['pixel_values'],
            input_ids=batch['input_ids'],
            attention_mask=batch['attention_mask']
        )
        
        # Predict actions
        predicted_actions = model.action_head(outputs.last_hidden_state[:, -1])
        
        # Compute loss
        loss = vla_loss(predicted_actions, batch['actions'])
        
        # Backward pass
        accelerator.backward(loss)
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()
        
        # Logging
        epoch_loss += loss.item()
        global_step += 1
        
        if global_step % 100 == 0:
            wandb.log({
                "loss": loss.item(),
                "learning_rate": scheduler.get_last_lr()[0],
                "epoch": epoch
            })
    
    print(f"Epoch {epoch+1} - Average Loss: {epoch_loss / len(train_loader):.4f}")
    
    # Save checkpoint
    if (epoch + 1) % 2 == 0:
        accelerator.save_model(model, f"checkpoints/epoch_{epoch+1}")

Step 7: Evaluate the Model

Simulation Evaluation

Python

def evaluate_in_simulation(model, env, num_episodes=50):
    """Evaluate model in simulation environment"""
    model.eval()
    success_count = 0
    
    with torch.no_grad():
        for episode in range(num_episodes):
            obs = env.reset()
            instruction = env.get_task_instruction()
            done = False
            
            while not done:
                # Process observation
                image = torch.tensor(obs['image']).unsqueeze(0)
                text_inputs = processor.tokenizer(instruction, return_tensors="pt")
                
                # Predict action
                outputs = model(
                    pixel_values=image.to(model.device),
                    input_ids=text_inputs['input_ids'].to(model.device)
                )
                action = model.action_head(outputs.last_hidden_state[:, -1])
                
                # Denormalize and execute
                action = denormalize_actions(action.cpu().numpy(), action_stats)
                obs, reward, done, info = env.step(action[0])
            
            if info['success']:
                success_count += 1
    
    success_rate = success_count / num_episodes
    print(f"Success Rate: {success_rate*100:.1f}%")
    return success_rate

Behavioral Cloning Metrics

Python

def evaluate_bc_metrics(model, val_loader):
    """Evaluate behavioural cloning performance"""
    model.eval()
    total_mse = 0
    total_cosine_sim = 0
    n_samples = 0
    
    with torch.no_grad():
        for batch in val_loader:
            outputs = model(
                pixel_values=batch['pixel_values'],
                input_ids=batch['input_ids']
            )
            pred_actions = model.action_head(outputs.last_hidden_state[:, -1])
            
            # MSE
            mse = F.mse_loss(pred_actions, batch['actions'])
            total_mse += mse.item() * len(batch['actions'])
            
            # Cosine similarity
            cosine_sim = F.cosine_similarity(pred_actions, batch['actions']).mean()
            total_cosine_sim += cosine_sim.item() * len(batch['actions'])
            
            n_samples += len(batch['actions'])
    
    return {
        'mse': total_mse / n_samples,
        'cosine_similarity': total_cosine_sim / n_samples
    }

Step 8: Deploy the Model

Model Export

Console

# Save fine-tuned model
model.save_pretrained("./finetuned_vla")
processor.save_pretrained("./finetuned_vla")

# For deployment, merge LoRA weights
from peft import PeftModel

base_model = AutoModel.from_pretrained(model_name)
merged_model = PeftModel.from_pretrained(base_model, "./finetuned_vla")
merged_model = merged_model.merge_and_unload()
merged_model.save_pretrained("./deployed_model")

Real-Time Inference

Python

class VLAController:
    def __init__(self, model_path):
        self.model = AutoModel.from_pretrained(model_path)
        self.processor = AutoProcessor.from_pretrained(model_path)
        self.model.eval()
        self.model.to('cuda')
    
    @torch.inference_mode()
    def predict_action(self, image, instruction):
        """Real-time action prediction"""
        # Preprocess
        inputs = self.processor(
            images=image,
            text=instruction,
            return_tensors="pt"
        ).to('cuda')
        
        # Predict
        outputs = self.model(**inputs)
        action = self.model.action_head(outputs.last_hidden_state[:, -1])
        
        return action.cpu().numpy()[0]

# Usage
controller = VLAController("./deployed_model")
action = controller.predict_action(camera_image, "pick up the cup")
robot.execute_action(action)

Resources

OpenVLA
RT-2 Paper and RT-2 website: This project introduced the term VLA, meaning Vision-Language-Action model.
PEFT Library
RoboMimic for dataset handling
OpenVLA: LeRobot Research Presentation #5 by Moo Jin Kim

Conclusion

Fine-tuning VLA models allows robots to carry out specialized tasks through natural language control. This tutorial has shown how pre-trained models can be adapted to specific hardware setups and tasks while benefiting from large-scale pre-training. A practical approach is to begin in simulation, improve the model through fast iteration, and then gradually move toward real robot hardware as performance becomes more reliable.

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Fine-Tune Vision-Language-Action Models for Robotics

What Are Vision-Language-Action Models?

Key Takeaways

Prerequisites

Hardware Requirements

Step 0: Set Up GPU Access and Jupyter Notebook

Step 1: Understand the VLA Architecture

Step 2: Prepare Your Dataset

Data Collection

Data Format Example

Dataset Organization

Step 3: Set Up the Base Model

Load a Pre-Trained VLA Model

Adapt the Action Space

Step 4: Use LoRA for Efficient Fine-Tuning

Step 5: Process and Augment the Data

Image Preprocessing

Action Normalization

DataLoader Setup

Step 6: Build the Training Loop

Loss Function

Training Script

Step 7: Evaluate the Model

Simulation Evaluation

Behavioral Cloning Metrics

Step 8: Deploy the Model

Model Export

Real-Time Inference

Resources

Conclusion

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