From a4bed41132a3f5ffb05579003a8fece26d188e56 Mon Sep 17 00:00:00 2001
From: Pepijn <138571049+pkooij@users.noreply.github.com>
Date: Fri, 3 Oct 2025 12:06:18 +0200
Subject: [PATCH] Improve docs pi (#2110)

* Improve docs and add numpy to pi install requirments

* fix formatting

* update command

* remvoe numpy dep
---
 docs/source/pi0.mdx  |  2 +-
 docs/source/pi05.mdx | 19 ++++++++++++++-----
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/docs/source/pi0.mdx b/docs/source/pi0.mdx
index 10260ee72..d36fe0ce4 100644
--- a/docs/source/pi0.mdx
+++ b/docs/source/pi0.mdx
@@ -49,7 +49,7 @@ policy.type=pi0
 For training π₀, you can use the standard LeRobot training script with the appropriate configuration:
 
 ```bash
-python src/lerobot/scripts/train.py \
+python src/lerobot/scripts/lerobot_train.py \
     --dataset.repo_id=your_dataset \
     --policy.type=pi0 \
     --output_dir=./outputs/pi0_training \
diff --git a/docs/source/pi05.mdx b/docs/source/pi05.mdx
index b777fcd58..b6267fc5e 100644
--- a/docs/source/pi05.mdx
+++ b/docs/source/pi05.mdx
@@ -51,13 +51,13 @@ policy.type=pi05
 Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:
 
 ```bash
-python src/lerobot/scripts/train.py \
+python src/lerobot/scripts/lerobot_train.py\
     --dataset.repo_id=your_dataset \
     --policy.type=pi05 \
-    --output_dir=./outputs/pi0_training \
-    --job_name=pi0_training \
-    --policy.repo_id=lerobot/pi05_base \
-    --policy.pretrained_path=your_repo_id \
+    --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 \
@@ -77,6 +77,15 @@ python src/lerobot/scripts/train.py \
   - [lerobot/pi05_base](https://huggingface.co/lerobot/pi05_base)
   - [lerobot/pi05_libero](https://huggingface.co/lerobot/pi05_libero) (specifically trained on the Libero dataset)
 
+If your dataset is not converted with `quantiles`, you can convert it with the following command:
+
+```bash
+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"}'`
+
 ## Performance Results
 
 ### Libero Benchmark Results