add pi05 doc

docs/usage/Pi05.md

@@ -0,0 +1,178 @@
+# OpenPI
+## Environment Setup
+
+We use [uv](https://docs.astral.sh/uv/) to manage Python dependencies,you can add uv your conda environment.
+
+```bash
+conda activate RoboTwin
+# Install uv
+pip install uv
+```
+Once uv is installed, run the following commands to set up the environment:
+
+```bash
+cd policy/pi05
+# Install prequisites in uv environment
+GIT_LFS_SKIP_SMUDGE=1 uv sync
+```
+
+If you want to eval pi05 policy in RoboTwin，you are required to install curobo in your uv environment：
+
+```bash
+conda deactivate
+source .venv/bin/activate
+# At this point, you should be in the (openpi) environment
+cd ../../envs
+git clone https://github.com/NVlabs/curobo.git
+cd curobo
+pip install -e . --no-build-isolation
+cd ../../policy/pi05/
+bash
+```
+
+## Generate RoboTwin Data
+See [RoboTwin Tutorial (Usage Section)](https://robotwin-platform.github.io/doc/usage/collect-data.html) for more details.
+
+## Generate openpi Data
+First, create the `processed_data` and `training_data` folders in the `policy/pi05` directory:
+
+```
+mkdir processed_data && mkdir training_data
+```
+
+Then, convert RoboTwin data to HDF5 data type.
+
+``` bash
+bash process_data_pi0.sh ${task_name} ${task_config} ${expert_data_num}
+# bash process_data_pi0.sh beat_block_hammer demo_clean 50
+# or processing randomized data: bash process_data.sh beat_block_hammer demo_randomized 50
+```
+
+If success, you will find the `${task_name}-${task_config}-${expert_data_num}` folder under `policy/pi05/processed_data`.
+
+Example folder structure:
+
+```bash
+processed_data/ 
+├──${task_name}-${task_config}-${expert_data_num}
+|       |   ├──episode_0
+|       |   |	├── instructions.json  
+|       |   |	├── episode_0.hdf5  
+|       |   ├── episode_1 
+|       |   |	├── instructions.json  
+|       |   |	├── episode_1.hdf5  
+|       |	├── ...
+```
+
+Copy all the data you wish to use for training from `processed_data` into `training_data/${model_name}`. If you have multiple tasks with different data, simply copy them in the same way.please Place the corresponding task folders according to the example below.
+
+```bash
+#multi-task dataset example
+training_data/  
+├── ${model_name}
+|       ├──${task_0}
+|       |   ├──episode_0
+|       |   |	├── instructions.json  
+|       |   |	├── episode_0.hdf5  
+|       |   ├── episode_1 
+|       |   |	├── instructions.json  
+|       |   |	├── episode_1.hdf5  
+|       |	├── ...
+|       ├── ${task_1}
+|       |   ├──episode_0
+|       |   |	├── instructions.json  
+|       |   |	├── episode_0.hdf5  
+|       |   ├── episode_1 
+|       |   |	├── instructions.json  
+|       |   |	├── episode_1.hdf5  
+|       |	├── ...
+
+#sigle task example
+training_data/  
+├── demo_clean
+|       ├──beat_block_hammer-demo_clean-50
+|       |   ├──episode_0
+|       |   |	├── instructions.json  
+|       |   |	├── episode_0.hdf5  
+|       |   ├── episode_1 
+|       |   |	├── instructions.json  
+|       |   |	├── episode_1.hdf5  
+|       |	├── ...
+```
+
+Before generating the LerobotDataset format data for pi05,please make sure you have enough disk space under the `~/.cache`.This is because generating the `lerobotdataset` will require a large amount of space.And the datasets will be writed into `$XDG_CACHE_HOME`,which default path is  `~/.cache`.If you don't have enough disk space under the `~/.cache` path, please use the following command to set a different cache directory with sufficient space:
+
+```bash
+export XDG_CACHE_HOME=/path/to/your/cache
+```
+
+Now, we can directly generate the LerobotDataset format data for pi05
+
+```bash
+# hdf5_path: The path to the generated HDF5 data (e.g., ./training_data/${model_name}/)
+# repo_id: The name of the dataset (e.g., my_repo)
+bash generate.sh ${hdf5_path} ${repo_id}
+#bash generate.sh ./training_data/demo_clean/ demo_clean_repo
+```
+
+LerobotDataset format data will be writed into `${XDG_CACHE_HOME}/huggingface/lerobot/${repo_id}`
+
+## Write the Corresponding `train_config`
+
+> For our official experiment, we use `pi0_base_aloha_robotwin_lora`
+
+In `src/openpi/training/config.py`, there is a dictionary called `_CONFIGS`. You can modify PI05 configurations I’ve written:
+`pi05_aloha_full_base`
+
+You only need to write `repo_id`  on your datasets.(e.g., `repo_id=demo_clean_repo`)
+If your do not have enough gpu memory, you can set `fsdp_devices`, refer to `config.py` line `src/openpi/training/config.py` line 526.
+
+## 5. Finetune model
+```bash
+# compute norm_stat for dataset
+uv run scripts/compute_norm_stats.py --config-name ${train_config_name}
+# uv run scripts/compute_norm_stats.py --config-name pi05_aloha_full_base
+
+# train_config_name: The name corresponding to the config in _CONFIGS, such as pi05_aloha_full_base
+# model_name: You can choose any name for your model
+# gpu_use: if not using multi gpu,set to gpu_id like 0;else set like 0,1,2,3
+bash finetune.sh ${train_config_name} ${model_name} ${gpu_use}
+#bash finetune.sh pi0_base_aloha_robotwin_full demo_clean 0,1,2,3
+```
+
+| Training mode | Memory Required | Example GPU        |
+| ------------------ | --------------- | ------------------ |
+| Fine-Tuning (LoRA) | > 46 GB       | A6000(48G)           |
+| Fine-Tuning (Full) | > 100 GB         | 2*A100 (80GB) / 2*H100 |
+
+If your GPU memory is insufficient, please set the `fsdp_devices` parameter according to the following GPU memory reference, or reduce the `batch_size` parameter.
+Or you can try setting `XLA_PYTHON_CLIENT_PREALLOCATE=false` in `finetune.sh`, it will cost lower gpu memory, but make training speed slower.
+
+The default `batch_size` is 32 in the table below.
+
+| GPU memory | Model type | GPU num |fsdp_devices | Example GPU |
+| ----- | ----- | ----- | ----- | ----- |
+|  24G | lora | 2 | 2 | 4090(24G)  |
+|  40G | lora | 2 | 2 | A100(40G)  |
+|  48G | lora | 1 | 1 | A6000(48G) |
+|  40G | full | 4 | 4 | A100(40G)  |
+|  80G | full | 2 | 2 | A100(80G)  |
+
+## Eval on RoboTwin
+
+Checkpoints will be saved in policy/pi05/checkpoints/${train_config_name}/${model_name}/${checkpoint_id}
+
+You can modify the `deploy_policy.yml` file to change the `checkpoint_id` you want to evaluate.
+
+```bash
+# ckpt_path like: policy/pi05/checkpoints/pi05_aloha_full_base/demo_clean/30000
+bash eval.sh ${task_name} ${task_config} ${train_config_name} ${model_name} ${seed} ${gpu_id}
+# bash eval.sh beat_block_hammer demo_clean pi05_aloha_full_base demo_clean 0 0
+# This command trains the policy using the `demo_clean` setting ($model_name)
+# and evaluates it using the same `demo_clean` setting ($task_config).
+
+# To evaluate a policy trained on the `demo_clean` setting and tested on the `demo_randomized` setting, run:
+# bash eval.sh beat_block_hammer demo_randomized pi05_aloha_full_base demo_clean 0 0
+```
+
+The evaluation results, including videos, will be saved in the `eval_result` directory under the project root.

mkdocs.yml

@@ -27,6 +27,7 @@ nav:
       - DP3: usage/DP3.md
       - RDT: usage/RDT.md
       - Pi0: usage/Pi0.md
+      - Pi05: usage/Pi05.md
       - DexVLA: usage/DexVLA.md
       - TinyVLA: usage/TinyVLA.md
       - OpenVLA-oft: usage/OpenVLA-oft.md

requirements.txt

@@ -5,4 +5,4 @@ mkdocs-add-number-plugin
 mkdocs-git-authors-plugin
 mkdocs-minify-plugin
 mkdocs-redirects
-jieba
+jieba