{
  "id": "octo-examples-02-finetune-new-observation-action-py",
  "name": "Octo: ALOHA observation/action adaptation fine-tuning",
  "summary": "Pinned didactic Octo fine-tuning entrypoint adapting observation and action heads with the explicitly named ALOHA simulated cube-handover dataset. Statically reviewed source only; not run and no Hub preparation adapter.",
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    "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/examples/02_finetune_new_observation_action.py",
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    "authors": [
      "Octo Model Team",
      "Robotic AI & Learning Lab Berkeley"
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    "spdx": "MIT",
    "url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/LICENSE",
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    "scope": "Selected first-party Python workflow source only. Preserve upstream copyright and per-file notices. Model weights, datasets, third-party code, robot drivers/firmware and services retain separate terms; no rights for those assets are inferred.",
    "bytes": 1091
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  "access": {
    "mode": "source_reference",
    "hub_prepare": false,
    "instructions": "Fetch the pinned repository, verify this source checksum, and use upstream installation and invocation. This record does not install or execute code.",
    "setup_url": "https://github.com/octo-models/octo/blob/241fb3514b7c40957a86d869fecb7c7fc353f540/README.md",
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  "requirements": {
    "python": "Python3.10 in README setup; no packaging minimum asserted.",
    "packages": [
      "Pinned Octo checkout",
      "JAX0.4.20, Flax0.7.5 and TensorFlow2.15.0 from requirements.txt",
      "Per-example simulator/robot-controller dependencies and reviewed checkpoints/data"
    ],
    "display": "Hardware: Uses JAX devices and batch/device-count divisibility. No hard GPU minimum or CPU validation is declared in this file; README offers GPU/TPU setup. Weights: Requires an explicit pretrained Octo checkpoint; script loads it through OctoModel.load_pretrained. Datasets: Docstring requires a separately downloaded/extracted example_sim_data.zip ALOHA simulation dataset. Services/auth: Unconditionally initializes W&B in main and logs training metrics. No specific API-key variable is named.",
    "execution_domain": "computation",
    "source_imports": [
      "absl",
      "flax",
      "jax",
      "octo.data.dataset",
      "octo.model.components.action_heads",
      "octo.model.components.tokenizers",
      "octo.model.octo_model",
      "octo.utils.jax_utils",
      "octo.utils.spec",
      "octo.utils.train_utils",
      "optax",
      "tensorflow",
      "tqdm",
      "wandb"
    ],
    "notes": "This is actual fine-tuning, not a data-download-only entrypoint. Dataset/model downloads were not performed. Code licensing does not establish model, checkpoint, dataset or service permission.",
    "declared_requirements": {
      "python": "Python3.10 in README setup; no packaging minimum asserted.",
      "weights": "Requires an explicit pretrained Octo checkpoint; script loads it through OctoModel.load_pretrained.",
      "datasets": "Docstring requires a separately downloaded/extracted example_sim_data.zip ALOHA simulation dataset.",
      "hardware": "Uses JAX devices and batch/device-count divisibility. No hard GPU minimum or CPU validation is declared in this file; README offers GPU/TPU setup.",
      "external_services_and_auth": "Unconditionally initializes W&B in main and logs training metrics. No specific API-key variable is named.",
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      "license_scopes": {
        "code": "MIT for selected first-party workflow source.",
        "weights": "README links separately hosted rail-berkeley Octo checkpoints. This bounded repository review did not inspect model cards or establish a weight license; repository MIT is limited to selected code.",
        "datasets": "README references Open X-Embodiment and an example ALOHA simulation dataset. Their individual terms were not inspected and are not covered by the code-license assertion.",
        "third_party_code": "External dependencies retain their own licenses."
      },
      "interpretation": "Pinned upstream declarations only; no dependency resolution, model/data access, compatibility inference or runtime validation was performed."
    }
  },
  "invocation": {
    "entrypoint": "python examples/02_finetune_new_observation_action.py --pretrained_path=<approved-checkpoint> --data_dir=<reviewed-data> --save_dir=<local-output>",
    "notes": "This is actual fine-tuning, not a data-download-only entrypoint. Dataset/model downloads were not performed."
  },
  "popularity": {
    "metric": "github_stars",
    "repository_stars": 1807,
    "checked_at": "2026-10-17",
    "meaning": "Dated repository interest, not robot-skill usage, compatibility or runtime success."
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  "evidence": {
    "level": "source_reviewed",
    "runtime_verification": "not_run",
    "checked_at": "2026-10-17",
    "inspection": "Verified exact source, applicable license and author/setup document bytes at a full Git commit; parsed the Python AST and reviewed this guarded operational entrypoint and its explicit license notices. No upstream code, installation, robot command, dataset, mesh or checkpoint was executed or loaded.",
    "entrypoint_kind": "python_main",
    "inspection_method": "automated_static",
    "robot_discovery": {
      "scope": "The selected example explicitly fine-tunes on simulated ALOHA cube-handover demonstrations and a bimanual action space. This is a dataset/embodiment discovery label, not a real-hardware deployment or generic Octo compatibility claim.",
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          "quote": "using a simulated ALOHA cube handover dataset",
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  "artifact": {
    "kind": "workflow",
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    "workflow_role": "policy-training"
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}