{
  "id": "policy_vla_branch",
  "display_name": "VLA / Policy Model Branch",
  "status": "planned_adapter",
  "model_family": "OpenVLA, openpi, GR00T, Octo, and related policy models",
  "default_model_id": null,
  "local_model_env": "POLICY_MODEL_DIR",
  "dataset_contract": "xperience10m_observation_action_v0",
  "training_objective": "observation_to_action_or_motion_policy",
  "split_policy": {
    "unit": "episode",
    "default_counts": {
      "train": 96,
      "val": 16,
      "test": 16
    },
    "leakage_guard": "action targets and normalization statistics must be fit on train episodes only"
  },
  "modalities": {
    "observations": [
      "egocentric video",
      "language instruction or task context",
      "optional depth/pose/mocap/IMU state"
    ],
    "candidate_targets": [
      "action label",
      "next action",
      "hand trajectory chunk",
      "contact state",
      "retargeted body or humanoid action",
      "robot-compatible action token"
    ],
    "excluded_inputs": [
      "visualization.rrd"
    ]
  },
  "entrypoints": {
    "selection_manifest": "scripts/omni/build_selection_episode_manifest.py",
    "neutral_index": "scripts/omni/export_model_neutral_window_index.py",
    "export": null,
    "train": null,
    "eval": null,
    "launcher": null,
    "validate": "scripts/omni/validate_omni_finetune_run.py"
  },
  "primary_metrics": [
    "action_accuracy",
    "next_action_accuracy",
    "contact_accuracy",
    "trajectory_mpjpe",
    "object_affordance_f1",
    "held_out_episode_count"
  ],
  "artifact_contract": {
    "checkpoint_gate": "policy_checkpoint_action_space_and_normalizer",
    "required_eval_files": [
      "metrics.json",
      "policy_predictions.jsonl",
      "trajectory_metrics.csv",
      "action_confusion_matrix.csv",
      "retargeting_audit.json",
      "RUN_REPORT.md"
    ],
    "required_training_files": [
      "training_metadata.json",
      "progress.jsonl",
      "action_space.json",
      "normalization_stats.json",
      "checkpoint_manifest.json"
    ],
    "public_package_allowed": [
      "metrics",
      "policy prediction summaries",
      "trajectory metric tables",
      "action confusion matrices",
      "action-space definitions",
      "normalization metadata",
      "retargeting audit summaries",
      "validation summaries"
    ],
    "public_package_forbidden": [
      "raw MP4",
      "annotation HDF5",
      "Rerun RRD",
      "private retargeting source files",
      "base-model weights",
      "full checkpoints",
      "large archives"
    ]
  },
  "extension_requirements": [
    "Define an explicit action space before policy fine-tuning.",
    "Implement target conversion from human egocentric motion to policy-compatible action tokens or trajectories.",
    "Fit action normalizers on train episodes only and save them with the run manifest.",
    "Add policy evaluation that separates classification, trajectory, and retargeting metrics."
  ]
}