{ "title": "Ropedia Xperience-10M Research Roadmap", "summary": "Staged path from the public-sample task lab to multi-episode held-out evaluation, foundation-model selection, and larger omni/world-model extensions.", "current_decision_point": "Keep the public-sample task suite as the development harness, stage the selected official Xperience-10M relay for the held-out Qwen3-Omni pilot, then branch into Cosmos 3 world modeling and policy-model experiments after the data staging path is stable.", "phases": [ { "id": "public_sample_task_lab", "name": "Public-Sample Task Lab", "status": "implemented", "entry_condition": "One public Xperience-10M sample episode is available.", "deliverables": [ "1161 aligned windows", "12 task contracts", "minimal baseline heads", "neural MLP heads", "modality atlas", "task walkthroughs", "derived figures" ], "completion_evidence": [ "PROJECT_STATUS.md", "EVALUATION_PROTOCOL.md", "RESEARCH_TAKEAWAYS.md", "docs/data/summary_metrics.json", "results/episode_task_suite/summary_report.json" ], "reader_takeaway": "The public sample supports task design, feature contracts, walkthroughs, and baseline comparisons." }, { "id": "multi_episode_data_staging", "name": "Multi-Episode Data Staging", "status": "active", "entry_condition": "Full-dataset access and enough storage for selected episodes.", "deliverables": [ "128 selected episodes", "episode manifest", "missing-view manifest", "held-out episode split", "source-discovery report" ], "completion_evidence": [ "results/omni_finetune/DATA_ACCESS_STATUS.md", "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md", "results/omni_finetune/source_discovery.json" ], "reader_takeaway": "The next scale decision is data staging, with train/test separation at the episode level." }, { "id": "qwen3_omni_lora_pilot", "name": "Qwen3-Omni LoRA Pilot", "status": "next", "entry_condition": "Selected episodes are staged locally with no train/test episode leakage.", "deliverables": [ "dataset JSONL/media manifests", "LoRA adapter checkpoint", "progress logs", "held-out predictions", "metrics", "confusion matrices", "run report" ], "completion_evidence": [ "dataset_manifest.json", "training_metadata.json", "progress.jsonl", "metrics.json", "predictions.jsonl", "RUN_REPORT.md" ], "reader_takeaway": "The first omni-model pilot should establish a complete held-out-episode training and evaluation loop." }, { "id": "foundation_model_selection_matrix", "name": "Foundation-Model Selection Matrix", "status": "next", "entry_condition": "The selected relay is staged or a 3-8 episode dry run is staged for preprocessing checks.", "deliverables": [ "backbone registry", "Cosmos 3 world-model branch plan", "Qwen3-Omni LoRA baseline plan", "OpenVLA/openpi/GR00T policy-branch candidates", "model-specific evaluation additions" ], "completion_evidence": [ "FOUNDATION_MODEL_PLAN.md", "docs/data/foundation_model_plan.json", "research_roadmap_interactive.json" ], "reader_takeaway": "Qwen3-Omni remains the first trainable held-out pilot; Cosmos 3 is the first world-model branch; VLA/policy models wait for explicit action targets." }, { "id": "robustness_run_64_128_episode", "name": "64-128 Episode Robustness Run", "status": "planned", "entry_condition": "The selected-episode pilot trains and evaluates cleanly.", "deliverables": [ "split-by-session metrics", "modality ablations", "calibration/object/language error analysis", "missing-view sensitivity analysis" ], "completion_evidence": [ "held-out metrics by session", "held-out metrics by task", "held-out metrics by modality", "ablation tables", "qualitative error analysis" ], "reader_takeaway": "The robustness run tests whether the pilot conclusions survive broader sessions and missing modalities." }, { "id": "foundation_world_model_extensions", "name": "Cosmos 3 and Policy-Model Extensions", "status": "planned", "entry_condition": "Enough multi-episode data, compute budget, and model-specific action/world-state targets.", "deliverables": [ "Cosmos 3 future-window or action-conditioned world-model probe", "OpenVLA/openpi/GR00T action-policy baseline", "audio/video/depth/pose/mocap conditioning audit", "affordance and object-interaction tasks", "synthetic-data usefulness test" ], "completion_evidence": [ "task-specific held-out evaluations", "qualitative inspection", "updated model cards" ], "reader_takeaway": "The long-term direction is richer multimodal representation learning for embodied-AI reasoning, with model branches chosen by task fit rather than by a single default backbone." } ], "public_surfaces_to_update": [ "README.md", "PROJECT_STATUS.md", "RESEARCH_TAKEAWAYS.md", "EVALUATION_PROTOCOL.md", "ARTIFACT_GUIDE.md", "docs/index.html", "docs/data/research_roadmap.json", "Hugging Face Space card", "Hugging Face artifact dataset card", "Hugging Face model card" ] }