π₀.₅ (Pi05) Policy

π₀.₅ is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository.

Model Overview

π₀.₅ represents a significant evolution from π₀, developed by Physical Intelligence to address a big challenge in robotics: open-world generalization. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training.

The Generalization Challenge

As Physical Intelligence explains, the fundamental challenge isn't performing tasks of agility or dexterity, but generalization, the ability to correctly perform tasks in new settings with new objects. Consider a robot cleaning different homes: each home has different objects in different places. Generalization must occur at multiple levels:

• Physical Level: Understanding how to pick up a spoon (by the handle) or plate (by the edge), even with unseen objects in cluttered environments
• Semantic Level: Understanding task semantics, where to put clothes and shoes (laundry hamper, not on the bed), and what tools are appropriate for cleaning spills
• Environmental Level: Adapting to "messy" real-world environments like homes, grocery stores, offices, and hospitals

Co-Training on Heterogeneous Data

The breakthrough innovation in π₀.₅ is co-training on heterogeneous data sources. The model learns from:

1. Multimodal Web Data: Image captioning, visual question answering, object detection
2. Verbal Instructions: Humans coaching robots through complex tasks step-by-step
3. Subtask Commands: High-level semantic behavior labels (e.g., "pick up the pillow" for an unmade bed)
4. Cross-Embodiment Robot Data: Data from various robot platforms with different capabilities
5. Multi-Environment Data: Static robots deployed across many different homes
6. Mobile Manipulation Data: ~400 hours of mobile robot demonstrations

This diverse training mixture creates a "curriculum" that enables generalization across physical, visual, and semantic levels simultaneously.

Installation Requirements

1. Install LeRobot by following our Installation Guide.

2. Install Pi0.5 dependencies by running:

   bashCopy

   pip install -e ".[pi]"
   

Usage

To use π₀.₅ in your LeRobot configuration, specify the policy type as:

policy.type=pi05

Training

Training Command Example

Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:

lerobot-train \
    --dataset.repo_id=your_dataset \
    --policy.type=pi05 \
    --output_dir=./outputs/pi05_training \
    --job_name=pi05_training \
    --policy.repo_id=your_repo_id \
    --policy.pretrained_path=lerobot/pi05_base \
    --policy.compile_model=true \
    --policy.gradient_checkpointing=true \
    --wandb.enable=true \
    --policy.dtype=bfloat16 \
    --policy.freeze_vision_encoder=false \
    --policy.train_expert_only=false \
    --steps=3000 \
    --policy.device=cuda \
    --batch_size=32

Key Training Parameters

• --policy.compile_model=true: Enables model compilation for faster training
• --policy.gradient_checkpointing=true: Reduces memory usage significantly during training
• --policy.dtype=bfloat16: Use mixed precision training for efficiency
• --batch_size=32: Batch size for training, adapt this based on your GPU memory
• --policy.pretrained_path=lerobot/pi05_base: The base π₀.₅ model you want to finetune, options are:
  • lerobot/pi05_base
  • lerobot/pi05_libero (specifically trained on the Libero dataset)

Training Parameters Explained

💡 Tip: Setting train_expert_only=true freezes the VLM and trains only the action expert and projections, allowing finetuning with reduced memory usage.

If your dataset is not converted with quantiles, you can convert it with the following command:

python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \
    --repo-id=your_dataset \

Or train pi05 with this normalization mapping: --policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'

Relative Actions

By default, π₀.₅ predicts absolute actions. You can enable relative actions so the model predicts offsets relative to the current robot state. This can improve training stability for certain setups.

To use relative actions, first recompute your dataset stats in relative space via the CLI:

lerobot-edit-dataset \
    --repo_id your_dataset \
    --operation.type recompute_stats \
    --operation.relative_action true \
    --operation.chunk_size 50 \
    --operation.relative_exclude_joints "['gripper']" \
    --push_to_hub true

Or equivalently in Python:

from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.dataset_tools import recompute_stats

dataset = LeRobotDataset("your_dataset")
recompute_stats(dataset, relative_action=True, chunk_size=50, relative_exclude_joints=["gripper"])
dataset.push_to_hub()

The chunk_size should match your policy's chunk_size (default 50 for π₀.₅). relative_exclude_joints lists joint names that should remain in absolute space (e.g. gripper commands). Use --push_to_hub true to upload the updated stats to the Hub.

Then train with relative actions enabled:

lerobot-train \
    --dataset.repo_id=your_dataset \
    --policy.type=pi05 \
    --policy.use_relative_actions=true \
    --policy.relative_exclude_joints='["gripper"]' \
    ...

Performance Results

Libero Benchmark Results

π₀.₅ has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the libero base model for an additional 6k steps on the Libero dataset and compared the results to the OpenPI reference results.

These results demonstrate π₀.₅'s strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the Libero section.

License

This model follows the Apache 2.0 License, consistent with the original OpenPI repository.