---
datasets: bdhillon/PI-0.5-11.19.2025-v3-quantiles
library_name: lerobot
license: apache-2.0
model_name: pi05
pipeline_tag: robotics
tags:
- robotics
- lerobot
- pi05
---

# Training Config:

```python
CONFIG = {
  # Dataset (pre-converted v3.0 format)
  "dataset_repo_id": "bdhillon/PI-0.5-11.19.2025-v3-quantiles",
  "dataset_root": os.path.expanduser("~/lerobot-training/dataset/PI-0.5-11.19.2025-v3-quantiles"),

  # Model
  "policy_type": "pi05",
  "pretrained_path": "lerobot/pi05_base",

  # HuggingFace upload settings
  "repo_id": "bdhillon/PIv1",
  "push_to_hub": True,

  # Training hyperparameters
  "batch_size": 4,
  "policy.dtype": "bfloat16",
  "policy.use_amp": True,
  "steps": 1500,           # ~3-4 epochs for 11 episodes with 6953 frames
  "eval_freq": 250,        # Evaluate every 250 steps
  "log_freq": 50,          # Log to WandB every 50 steps
  "save_freq": 250,        # Save checkpoint every 250 steps

  # Evaluation settings
  "eval_n_episodes": 5,
  "eval_batch_size": 5,    # Must be <= eval_n_episodes

  # Output
  "output_dir": "./PIv1",

  # Logging
  "wandb_enable": True,
}
```
---

**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.

For more details, see the [Physical Intelligence π₀.₅ blog post](https://www.physicalintelligence.company/blog/pi05).

---

### Train From Scratch

```bash
lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true
```

_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._

### Evaluate / Run inference

```bash
lerobot-record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
  --episodes=10
```

Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.

---