docs/source/pi05.mdx
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.
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.
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:
The breakthrough innovation in π₀.₅ is co-training on heterogeneous data sources. The model learns from:
This diverse training mixture creates a "curriculum" that enables generalization across physical, visual, and semantic levels simultaneously.
Install LeRobot by following our Installation Guide.
Install Pi0.5 dependencies by running:
pip install -e ".[pi]"
If you installed LeRobot from PyPI:
pip install 'lerobot[pi]'
To use π₀.₅ in your LeRobot configuration, specify the policy type as:
policy.type=pi05
Finetune the LIBERO base model on lerobot/libero, a ~1.9 GB video-encoded copy of the demonstrations behind the results below.
It carries the keys π₀.₅ reads, which are also the ones the LIBERO environment produces at evaluation time:
| Feature | Shape in the dataset | How π₀.₅ consumes it |
|---|---|---|
observation.images.image | 256×256×3, agentview | resized to 224×224 |
observation.images.image2 | 256×256×3, wrist | resized to 224×224 |
observation.state | 8 | discretized into 256 bins and written into the prompt |
action | 7 | padded to 32 internally; the loss uses the first 7 dims |
No --rename_map is needed here — the keys already match; see Rename Map and Empty Cameras if yours differ.
Sized for a single 80 GB GPU:
lerobot-train \
--dataset.repo_id=lerobot/libero \
--policy.type=pi05 \
--policy.pretrained_path=lerobot/pi05_libero_base \
--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}' \
--policy.n_action_steps=10 \
--policy.empty_cameras=1 \
--policy.freeze_vision_encoder=false \
--policy.train_expert_only=false \
--policy.gradient_checkpointing=true \
--policy.dtype=bfloat16 \
--policy.device=cuda \
--policy.push_to_hub=false \
--output_dir=./outputs/pi05_libero \
--job_name=pi05_libero \
--batch_size=64 \
--num_workers=8 \
--steps=30000 \
--save_freq=5000 \
--seed=1000
Mean/std normalization, not π₀.₅'s quantile default — matching pi05_libero_finetuned_v044, the checkpoint the results below were measured on.
--policy.n_action_steps=10 and --policy.empty_cameras=1 are explicit because --policy.pretrained_path loads weights only — lerobot/pi05_libero_base stores both, and they would otherwise fall back to 50 and 0 (see Loading a checkpoint).
Then evaluate a checkpoint with lerobot-eval and compare against the reference success rates — see LIBERO.
π₀.₅ normalizes STATE and ACTION with quantiles, so your dataset's meta/stats.json needs q01 and q99. Older datasets carry only min/max/mean/std and fail on the first batch:
ValueError: QUANTILES normalization mode requires q01 and q99 stats
Recompute them:
lerobot-edit-dataset \
--repo_id your_dataset \
--new_repo_id your_dataset \
--operation.type recompute_stats \
--operation.overwrite true
The result lands in $HF_LEROBOT_HOME/your_dataset, not the cache --dataset.repo_id reads — so train with --dataset.root=$HF_LEROBOT_HOME/your_dataset, or add --push_to_hub true above.
Or keep the dataset as-is and pass --policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'.
The same finetune with the VLM frozen: less memory, at some cost in success rate. Swap --dataset.repo_id for your own dataset.
lerobot-train \
--dataset.repo_id=lerobot/libero \
--policy.type=pi05 \
--policy.pretrained_path=lerobot/pi05_libero_base \
--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}' \
--policy.n_action_steps=10 \
--policy.empty_cameras=1 \
--policy.freeze_vision_encoder=true \
--policy.train_expert_only=true \
--policy.gradient_checkpointing=true \
--policy.dtype=bfloat16 \
--policy.device=cuda \
--policy.push_to_hub=false \
--output_dir=./outputs/pi05_libero_expert \
--job_name=pi05_libero_expert \
--batch_size=64 \
--num_workers=8 \
--steps=30000 \
--save_freq=5000 \
--seed=1000
--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=64: 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:
The two forms are not interchangeable:
--policy.path | --policy.pretrained_path | |
|---|---|---|
| Loads | weights and the checkpoint's config.json | weights only |
| Feature names | from the checkpoint | from your dataset |
Stored settings, e.g. n_action_steps | inherited | reset to the defaults |
--policy.type | must be omitted | required |
--rename_map | needed when your camera keys differ | never — the keys come from your data |
Passing a --rename_map alongside --policy.pretrained_path renames the batch away from those names, and the first batch fails with All image features are missing from the batch.
| 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.
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"]' \
...
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.
This model follows the Apache 2.0 License, consistent with the original OpenPI repository.