Instructions to use felixw/itps-act with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use felixw/itps-act with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| library_name: lerobot | |
| license: mit | |
| tags: | |
| - lerobot | |
| - act | |
| - robotics | |
| - maze2d | |
| - itps | |
| - pytorch_model_hub_mixin | |
| pipeline_tag: robotics | |
| # ITPS Maze2D — Action Chunking Transformer (ACT) | |
| Pre-trained Action Chunking Transformer checkpoint used in | |
| **Inference-Time Policy Steering through Human Interactions** | |
| ([paper](https://huggingface.co/papers/2411.16627), [project page](https://yanweiw.github.io/itps/), [code](https://github.com/yanweiw/itps)). | |
| The model was trained on the [D4RL Maze2D](https://github.com/Farama-Foundation/D4RL) | |
| dataset and is intended to be loaded with the | |
| [LeRobot](https://github.com/huggingface/lerobot) policy classes. | |
| ## Usage | |
| Clone the inference repo, then load this checkpoint directly from the Hub: | |
| ```bash | |
| git clone https://github.com/yanweiw/itps.git && cd itps | |
| pip install -e . | |
| python interact_maze2d.py -p act --hf | |
| ``` | |
| Or load it programmatically: | |
| ```python | |
| from itps.common.policies.act.modeling_act import ACTPolicy | |
| policy = ACTPolicy.from_pretrained("felixw/itps-act") | |
| policy.eval() | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @article{wang2024itps, | |
| title={Inference-Time Policy Steering through Human Interactions}, | |
| author={Wang, Yanwei and others}, | |
| journal={arXiv preprint arXiv:2411.16627}, | |
| year={2024} | |
| } | |
| ``` | |
| ## License | |
| MIT — see [LICENSE](https://github.com/yanweiw/itps/blob/main/LICENSE). | |