Fine-Tuning the Pi0 Model: Adapting to Robot Tasks with Custom Datasets

Pi0 is an advanced Vision-Language-Action (VLA) model that can quickly adapt to specific robot tasks through few-shot fine-tuning.

This document is based on the LeRobot framework and uses the pre-trained Pi0 model (lerobot/pi0) for fine-tuning on custom robot datasets.

Prerequisites

Have a dataset in LeRobot format (refer to the LeRobot dataset export guide in the previous section)
Need to adapt the Pi0 model to specific robot hardware, tasks, or control strategies
Familiar with PyTorch and the Hugging Face ecosystem training process
Basic experience in deep learning model training

Fine-Tuning Overview

Fine-tuning is the process of further training a pre-trained model using domain-specific data, typically involving fewer iterations.

The Pi0 model has been pre-trained on diverse robot tasks, possessing general visual perception, language understanding, and action generation capabilities.

Through fine-tuning, Pi0 can achieve:

Environmental Adaptation: Adjust to specific camera views, lighting conditions, and mechanical structures
Task Specialization: Optimize performance for specific tasks (e.g., object grasping, classification, or placement)
Precision Improvement: Significantly enhance control accuracy and success rate in target tasks

In simple terms: Pre-trained models know a lot, but not necessarily about you; after fine-tuning, they understand you.

Environment Preparation

System Requirements

Ensure your environment meets the following requirements:

Python ≥ 3.8 (recommended 3.10 or higher)
GPU: At least 32GB VRAM (Pi0 is a large-scale model, recommend NVIDIA V100 or higher performance GPU)
Memory: At least 64GB system RAM
Storage: Sufficient disk space for datasets and model checkpoints

Install Dependencies

# Clone LeRobot repository (primary)
git clone https://github.com/lerobot-ai/lerobot.git
cd lerobot/

# Install LeRobot framework (including Pi0 support)
pip install -e ".[pi0]"

# Verify installation
python -c "from lerobot.policies import Pi0Policy; print('Pi0 installed successfully!')"

Mirror (if needed): https://github.com/huggingface/lerobot

Prepare Your Dataset

Export LeRobot Format Data

You can export annotated data to LeRobot format dataset through the IO Data Platform export page, see Export LeRobot Dataset.

Important Note: You don't need to upload your data to HuggingFace for training; use the --dataset.root= parameter to specify local directory data for fine-tuning.

Dataset Structure Example

Assume your exported data is stored in ~/DualPiper_Pickup_Pen:

$ ls ~/DualPiper_Pickup_Pen
data  meta  videos

$ ls ~/DualPiper_Pickup_Pen/data
episode_000  episode_001  episode_002  ...

$ ls ~/DualPiper_Pickup_Pen/data/episode_000
observations  actions.npy  language_instruction.txt  metadata.json

Start Fine-Tuning

Basic Training Command

Use the train.py script for training:

# Set CUDA memory allocation policy (recommended)
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

# Start training
python3 -m lerobot.scripts.train \
  --dataset.root=~/DualPiper_Pickup_Pen \
  --dataset.repo_id=io-ai-data/DualPiper_Pickup_Pen \
  --policy.path=lerobot/pi0 \
  --policy.repo_id=lerobot/pi0 \
  --policy.device=cuda \
  --output_dir=./checkpoints/pi0_finetuned \
  --batch_size=1 \
  --policy.attention_implementation=flash_attention_2 \
  --training.learning_rate=1e-5 \
  --training.num_epochs=10

tip

The Pi0 model has a large number of parameters, single GPU training may take tens of hours. For example, fine-tuning a 2-hour dataset takes about 50 hours on a single NVIDIA V100, while using 8 V100s can reduce it to about 10 hours.

Parameter Details

Parameter	Meaning	Recommended Value	Description
`--dataset.root`	Local dataset path	`~/your_dataset`	Points to LeRobot format dataset directory
`--dataset.repo_id`	Hugging Face dataset ID	`your-username/dataset`	For metadata identification
`--policy.path`	Pre-trained model path	`lerobot/pi0`	Specify official pre-trained model
`--policy.repo_id`	Model repository ID	`lerobot/pi0`	Hugging Face model repository
`--policy.device`	Training device	`cuda`	Enable GPU acceleration
`--output_dir`	Output directory	`./checkpoints/pi0_finetuned`	Fine-tuned model save path
`--batch_size`	Batch size	`1`	Adjust based on VRAM
`--policy.attention_implementation`	Attention mechanism	`flash_attention_2`	Use efficient implementation to accelerate training
`--training.learning_rate`	Learning rate	`1e-5`	Use lower value during fine-tuning to avoid catastrophic forgetting
`--training.num_epochs`	Number of training epochs	`10`	Adjust based on dataset scale

Multi-GPU Accelerated Training

If you have multiple GPUs, parallel training can significantly speed up the process.

# Train with 8 GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
torchrun --nproc_per_node=8 --master_port=29500 \
    -m lerobot.scripts.train \
    --dataset.root=/data/nfs2/export/lerobot/DualPiper_Pickup_Pen/ \
    --dataset.repo_id=io-ai-data/DualPiper_Pickup_Pen \
    --policy.path=lerobot/pi0 \
    --policy.repo_id=lerobot/pi0 \
    --policy.device=cuda \
    --output_dir=/home/puxk/DualPiper_Pickup_Pen_pi0_model \
    --batch_size=1 \
    --num_workers=4 \
    --policy.attention_implementation=flash_attention_2

The total batch size is nproc_per_node × batch_size. With --batch_size=1, 8 cards equal a total batch size of 8; increase if memory allows.

For debugging, suggest starting with --nproc_per_node=2 to test, then expand to all 8 cards.

Special Notes

Network Access: Downloading Hugging Face models may require proxy tools like hf-mirror
VRAM Monitoring: Monitor GPU utilization in real-time during training
Checkpoint Recovery: If training is interrupted, resume from the latest checkpoint in --output_dir

The Pi0 model integrates Google PaliGemma for language processing. As a recent Hugging Face model:

Version Compatibility

Transformers: Requires ≥ 4.37.0
Compatibility Check: If encountering embed_tokens errors, upgrade transformers

Solutions

# Upgrade to latest transformers
pip install -U transformers

# Or specify version
pip install transformers>=4.37.0

Policy Selection (Pi0 vs SmolVLA)

For single-task, short-horizon, high-rate control with modest language needs: Pi0/ACT-style policies can be more compute-efficient and responsive.
For stronger multi-task and cross-scene generalization or single-GPU/consumer hardware alignment: prefer SmolVLA; fine-tuning smolvla_base typically yields stable gains.
Under resource constraints: enable mixed precision, gradient accumulation, and consider lowering input resolution and batch size.

Training Results Inspection

Model Saving

After training, model files are located at:

./checkpoints/pi0_finetuned/
├── config.json
├── pytorch_model.bin
├── tokenizer.json
└── training_args.bin

Load Fine-Tuned Model

from lerobot import Pi0Policy  # Note: Import path may be lerobot.policies

# Load model
policy = Pi0Policy.from_pretrained("./checkpoints/pi0_finetuned")

# Inference example
action = policy.select_action(observation_batch)

Log Analysis

Training logs include:

Loss Curves: Training/validation loss trends
Learning Rate: Scheduler changes
Gradient Statistics: Norms and clipping information
Resource Utilization: GPU/CPU memory usage

Performance Evaluation

IO Data Platform Evaluation

The IO platform provides model inference quality evaluation, supporting visual comparison of real data with model outputs, including per-joint command comparisons.

Other Evaluation Methods

Quantitative Metrics

Action Error: Mean Absolute Error (MAE) or Mean Squared Error (MSE) between predicted actions and ground truth
Trajectory Similarity: Compare trajectories using Dynamic Time Warping (DTW) or Fréchet distance
Validation Loss: Monitor loss function values on the validation set

Qualitative Evaluation

Real-World Testing: Deploy to physical robots and measure task success rates
Behavior Analysis: Visualize if generated action sequences are reasonable
Human Evaluation: Collect expert subjective scores on model outputs

Evaluation Script Example

import numpy as np
from lerobot import Pi0Policy
from torch.utils.data import DataLoader  # Assume test_dataset is a DataLoader

# Load model
policy = Pi0Policy.from_pretrained("./checkpoints/pi0_finetuned")

# Evaluation function
def evaluate_model(policy, test_loader):
    total_mae = 0.0
    num_batches = 0
    
    for batch in test_loader:
        with torch.no_grad():
            predicted_action = policy.select_action(batch)
        true_action = batch['actions']
        
        # Calculate MAE
        mae = np.mean(np.abs(predicted_action.cpu().numpy() - true_action.cpu().numpy()))
        total_mae += mae
        num_batches += 1
    
    return total_mae / num_batches

# Perform evaluation (assume test_loader is prepared)
avg_mae = evaluate_model(policy, test_loader)
print(f"Average Absolute Error (MAE): {avg_mae:.4f}")

Prerequisites​

Fine-Tuning Overview​

Environment Preparation​

System Requirements​

Install Dependencies​

Prepare Your Dataset​

Export LeRobot Format Data​

Dataset Structure Example​

Start Fine-Tuning​

Basic Training Command​

Parameter Details​

Multi-GPU Accelerated Training​

Special Notes​

Version Compatibility​

Solutions​

Policy Selection (Pi0 vs SmolVLA)​

Training Results Inspection​

Model Saving​

Load Fine-Tuned Model​

Log Analysis​

Performance Evaluation​

IO Data Platform Evaluation​

Other Evaluation Methods​

Quantitative Metrics​

Qualitative Evaluation​

Evaluation Script Example​

Related Resources​