Instructions to use Efficient-Large-Model/Long-WAM-LIBERO-COD-LeRobot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Efficient-Large-Model/Long-WAM-LIBERO-COD-LeRobot with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Long-WAM LIBERO COD โ LeRobot
LeRobot-native version of Long-WAM LIBERO COD. COD jointly denoises future video and actions. This checkpoint uses 48 control ticks of observation history.
Use a LeRobot version with Long-WAM policy support and the longwam and libero extras installed.
The repository includes the complete policy weights, tokenizer, configuration, and preprocessing statistics.
Evaluation
lerobot-eval \
--policy.path=Efficient-Large-Model/Long-WAM-LIBERO-COD-LeRobot \
--policy.device=cuda \
--env.type=libero --env.task=libero_spatial --eval.batch_size=1
For direct inference, load LongWAMPolicy.from_pretrained(...) with the saved pre/post-processors.
Call select_action on every control tick and reset() between episodes.
Use the saved camera order and action/state definitions. Execution is synchronous.
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