| { |
| "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." |
| ] |
| } |
|
|
