| --- |
| license: apache-2.0 |
| tags: |
| - robotics |
| - pi0.5 |
| - vlabench |
| - vision-language-action |
| - openpi |
| --- |
| |
| # pi05-posttrain-vlabench-primitive-aligned |
|
|
| Pi0.5 implementation trained on VLABench datasets. |
|
|
| This repository provides a Pi0.5 checkpoint trained with the VLABench pretrain primitive tasks dataset. The dataset contains 2,000 trajectories for each task. This config uses the aligned delta chunk setting. |
|
|
| The uploaded checkpoint includes both inference parameters and training/evaluation assets: |
|
|
| - `params/`: Orbax parameters for inference/evaluation. |
| - `train_state/`: full Orbax training state for resuming training. |
| - `assets/`: normalization statistics and checkpoint assets. |
| - `_CHECKPOINT_METADATA`: Orbax checkpoint metadata. |
|
|
| ## Evaluation |
|
|
| To run this checkpoint, please clone this repo: https://github.com/Shiduo-zh/openpi, and checkout to the branch `main`. Assume that you download this checkpoint and put it in the directory `checkpoints`, to run the policy as server, please run: |
|
|
| ```sh |
| bash serve_policy.sh pi05_posttrain_vlabench_primitive_aligned checkpoints/pi05-posttrain-vlabench-primitive-aligned/ |
| ``` |
|
|
| After serving the policy, open another terminal and run: |
|
|
| ```sh |
| bash multi_run_vlabench.sh <Your path to store the evaluate results> |
| ``` |
|
|
| ## Train |
|
|
| To reproduce the training result, please run the training script with the config `pi05_posttrain_vlabench_primitive_aligned`. |
|
|
| ```sh |
| XLA_PYTHON_CLIENT_MEM_FRACTION=0.95 uv run scripts/train.py pi05_posttrain_vlabench_primitive_aligned \ |
| --exp-name=pi05_posttrain_vlabench_primitive_aligned \ |
| --batch-size=32 \ |
| --save_interval=10000 \ |
| --overwrite |
| ``` |
|
|
| This checkpoint is trained for 200k iterations on the VLABench pretrain primitive tasks dataset with the aligned delta chunk setting. |
|
|
| ## Reference Results |
|
|
| Reference success rates are not included in this checkpoint card. Please run the evaluation script above to reproduce results for your environment. |
|
|
| ## Citation |
|
|
| If you use this checkpoint, please consider citing VLABench: |
|
|
| ```bibtex |
| @article{zhang2024vlabench, |
| title={Vlabench: A large-scale benchmark for language-conditioned robotics manipulation with long-horizon reasoning tasks}, |
| author={Zhang, Shiduo and Xu, Zhe and Liu, Peiju and Yu, Xiaopeng and Li, Yuan and Gao, Qinghui and Fei, Zhaoye and Yin, Zhangyue and Wu, Zuxuan and Jiang, Yu-Gang and others}, |
| journal={arXiv preprint arXiv:2412.18194}, |
| year={2024} |
| } |
| ``` |
|
|