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Fisher-Wang/pi05-droid-pytorch
pi05-droid-pytorch is a machine learning model from Fisher-Wang. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
PyTorch conversion of Physical Intelligence's official pi05droid checkpoint (gs://openpi-assets/checkpoints/pi05droid), converted via openpi examples/convertjaxmodeltopytorch.py with --configname pi05libero, precision…
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From the Hugging Face model README
PyTorch conversion of Physical Intelligence's official pi05_droid checkpoint
(gs://openpi-assets/checkpoints/pi05_droid), converted via openpi
examples/convert_jax_model_to_pytorch.py with --config_name pi05_libero,
precision float32. Uploaded for RobotTrain subnet validator pipeline testing —
a DROID-fine-tuned model is expected to score low on LIBERO, serving as a
format-valid negative control (zero-score anchor) for benchmark discrimination tests.
Note: besides the checkpoint's original DROID assets,
assets/physical-intelligence/libero/norm_stats.json from the official
pi05_libero checkpoint was added so the model can be served with the
pi05_libero config (the validator's submission convention).
Important: the LIBERO score of this repo does NOT measure the original checkpoint's capability. The injected LIBERO norm_stats differ from the statistics this model was trained with, and its native state/action spaces do not match LIBERO's (8-dim EEF state / 7-dim delta-EEF actions), so under this pipeline its inputs and outputs are semantically mismatched by construction. Treat it strictly as a format-valid negative control for validator pipeline testing — never as an evaluation of the underlying model.
Layout: model.safetensors (fp32) + assets/<asset_id>/norm_stats.json.
Load with openpi: create_trained_policy(get_config("pi05_libero"), <this dir>)
(LIBERO convention) or get_config("pi05_droid") with the original DROID stats.