π₀.₅ (Pi05) Policy#

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This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

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:

Multimodal Web Data: Image captioning, visual question answering, object detection
Verbal Instructions: Humans coaching robots through complex tasks step-by-step
Subtask Commands: High-level semantic behavior labels (e.g., “pick up the pillow” for an unmade bed)
Cross-Embodiment Robot Data: Data from various robot platforms with different capabilities
Multi-Environment Data: Static robots deployed across many different homes
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#

Install LeRobot by following our Installation Guide.
Install Pi0.5 dependencies by running:
```
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#

Parameter	Default	Description
`freeze_vision_encoder`	`false`	Do not freeze the vision encoder
`train_expert_only`	`false`	Do not freeze the VLM, train all parameters

💡 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/scripts/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 import LeRobotDataset, 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.

Benchmark	LeRobot Implementation	OpenPI Reference
Libero Spatial	97.0%	98.8%
Libero Object	99.0%	98.2%
Libero Goal	98.0%	98.0%
Libero 10	96.0%	92.4%
Average	97.5%	96.85%

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.