{ "title": "Ropedia Xperience-10M Unified 20-Task Suite", "status": "pass", "generated_at_utc": "2026-06-21T15:21:12+00:00", "task_count": 20, "task_count_summary": { "total_unified_tasks": 20, "public_framing": "all 20 task contracts are presented as one suite", "legacy_provenance_rows": 8 }, "unification_policy": { "public_framing": "The suite is presented as one 20-task benchmark surface. All task contracts share the same window, split, feature, baseline, and leakage-control language.", "legacy_path_note": "The directory and file name tier2_task_suite are retained only for backward-compatible artifact links; they are not a separate public benchmark tier." }, "dataset_scope": { "sample_episode_count": 1, "annotation": "data/sample/xperience-10m-sample/annotation.hdf5", "num_frames": 5821, "num_windows": 1161, "feature_dim": 8546, "window_frames": 20, "stride_frames": 5, "split_policy": "single_episode_chronological_70_30", "raw_hdf5_required_for_full_public_regeneration": true, "raw_data_redistributed": false }, "setup_alignment": { "same_window_unit": "20-frame aligned windows", "same_stride": "5 frames", "same_feature_manifest": "results/episode_task_suite/feature_manifest.json", "same_shared_tensor": "results/episode_task_suite/shared_windows.npz", "same_split": "chronological 70/30 train/test split within the public sample episode", "same_baseline_pattern": "minimal interpretable heads plus compact neural MLP heads", "same_leakage_policy": "Target-side future, contact, object, caption, relation, and interaction signals are excluded from inputs unless language is explicitly the query." }, "source_files": [ "docs/data/summary_metrics.json", "docs/data/task_walkthroughs.json", "docs/data/tier2_task_suite.json", "results/episode_task_suite/summary_report.json", "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json", "results/episode_task_suite/windows.csv", "results/episode_task_suite/feature_manifest.json" ], "tasks": [ { "task_id": "timeline_action", "task_display_name": "Action Recognition", "research_name": "Egocentric Action Recognition", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "supervised", "architecture_family": "multiclass classifier", "primary_direction": "C. Egocentric Vision & Interaction", "input": "One 20-frame window represented by the current feature vector: video/audio/depth summaries, pose, SLAM/camera pose, motion capture, IMU, calibration, and language-derived context.", "input_short": "20-frame multimodal window", "process": "window features -> action label builder -> classifier", "output": "A single action class for the current window.", "output_short": "current action class", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.05, "neural_primary_metric": 0.014814814814814814, "counts": { "num_windows": 1144, "num_eval_windows": 343, "num_train_windows": 801, "num_test_windows": 343, "num_classes": 18 }, "meaning": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/timeline_action.md", "minimal_metrics": "results/episode_task_suite/timeline_action/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json" }, "task_number": 1, "suite_label": "Task 01" }, { "task_id": "timeline_subtask", "task_display_name": "Procedure Step Recognition", "research_name": "Temporal Subtask Recognition", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "supervised", "architecture_family": "multiclass classifier", "primary_direction": "C. Egocentric Vision & Interaction", "input": "The same all-modality window vector used by action recognition.", "input_short": "20-frame multimodal window", "process": "window features -> subtask label builder -> classifier", "output": "A single subtask label for the current window.", "output_short": "current procedure step", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.05056355513846935, "neural_primary_metric": 0.02810810810810811, "counts": { "num_windows": 1147, "num_eval_windows": 344, "num_train_windows": 803, "num_test_windows": 344, "num_classes": 14 }, "meaning": "Recognize the broader activity stage so fine actions become a readable procedure timeline.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/timeline_subtask.md", "minimal_metrics": "results/episode_task_suite/timeline_subtask/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json" }, "task_number": 2, "suite_label": "Task 02" }, { "task_id": "transition_detection", "task_display_name": "Action Boundary Detection", "research_name": "Temporal Action Segmentation", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "diagnostic", "architecture_family": "binary classifier", "primary_direction": "C. Egocentric Vision & Interaction", "input": "One all-modality window vector plus labels derived from action-change timestamps.", "input_short": "current window with boundary target", "process": "action changes -> boundary labels -> binary classifier", "output": "A binary label: boundary or steady.", "output_short": "boundary or steady", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.6118237590630229, "neural_primary_metric": 0.5862068965517241, "counts": { "num_windows": 1161, "num_eval_windows": 348, "num_train_windows": 813, "num_test_windows": 348, "num_classes": 2 }, "meaning": "Detect the local moment where the episode changes from one action segment to the next.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/transition_detection.md", "minimal_metrics": "results/episode_task_suite/transition_detection/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json" }, "task_number": 3, "suite_label": "Task 03" }, { "task_id": "next_action", "task_display_name": "Next-Action Prediction", "research_name": "Short-Horizon Intention Prediction", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "supervised", "architecture_family": "future-label classifier", "primary_direction": "C. Egocentric Vision & Interaction", "input": "The current all-modality window vector at time t.", "input_short": "current window at time t", "process": "current features -> future label shift -> classifier", "output": "A single action class for t+20 frames.", "output_short": "action at t+20 frames", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.05925925925925927, "neural_primary_metric": 0.04186046511627907, "counts": { "num_windows": 1161, "num_eval_windows": 348, "num_train_windows": 813, "num_test_windows": 348, "num_classes": 18 }, "meaning": "Forecast the near-future action from the current observations only.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/next_action.md", "minimal_metrics": "results/episode_task_suite/next_action/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/next_action/metrics.json" }, "task_number": 4, "suite_label": "Task 04" }, { "task_id": "hand_trajectory_forecast", "task_display_name": "Hand Trajectory Forecasting", "research_name": "3D Hand Motion Forecasting", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "forecast", "architecture_family": "continuous regressor", "primary_direction": "A. Human Modeling & Motion Understanding", "input": "The current all-modality window vector at time t.", "input_short": "current multimodal window", "process": "current features -> future mocap target -> regression head", "output": "A future trajectory vector for left and right hand joints.", "output_short": "future hand-joint trajectory", "metric_key": "mpjpe", "metric_name": "MPJPE", "metric_direction": "lower", "minimal_primary_metric": 0.8646570444107056, "neural_primary_metric": 0.10785018652677536, "counts": { "num_windows": 1159, "num_train_windows": 811, "num_test_windows": 348 }, "meaning": "Predict the future 3D left/right hand path from the current multimodal state.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/hand_trajectory_forecast.md", "minimal_metrics": "results/episode_task_suite/hand_trajectory_forecast/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json" }, "task_number": 5, "suite_label": "Task 05" }, { "task_id": "contact_prediction", "task_display_name": "Contact State Prediction", "research_name": "Human-Object Contact Prediction", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "supervised", "architecture_family": "binary classifier", "primary_direction": "A. Human Modeling & Motion Understanding", "input": "Non-contact and non-caption feature blocks, so the answer is not directly leaked from the target labels.", "input_short": "non-contact, non-caption features", "process": "feature filter -> contact target -> binary classifier", "output": "A binary contact label.", "output_short": "contact or no contact", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 1.0, "neural_primary_metric": 1.0, "counts": { "num_windows": 1161, "num_eval_windows": 348, "num_train_windows": 813, "num_test_windows": 348, "num_classes": 1 }, "meaning": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/contact_prediction.md", "minimal_metrics": "results/episode_task_suite/contact_prediction/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json" }, "task_number": 6, "suite_label": "Task 06" }, { "task_id": "object_relevance", "task_display_name": "Object Relevance Prediction", "research_name": "Object-Centric Interaction Recognition", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "supervised", "architecture_family": "multi-label classifier", "primary_direction": "C. Egocentric Vision & Interaction", "input": "Non-caption feature blocks, so the model must infer objects from sensors rather than copying the caption words.", "input_short": "non-caption multimodal features", "process": "object vocabulary -> multi-hot labels -> sigmoid heads", "output": "A multi-label object set for the current window.", "output_short": "relevant object set", "metric_key": "micro_f1", "metric_name": "micro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.18034382095361662, "neural_primary_metric": 0.1679279279279279, "counts": { "num_windows": 1161, "num_train_windows": 813, "num_test_windows": 348 }, "meaning": "Infer which objects are relevant to the current manipulation window from non-caption features.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/object_relevance.md", "minimal_metrics": "results/episode_task_suite/object_relevance/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json" }, "task_number": 7, "suite_label": "Task 07" }, { "task_id": "caption_grounding", "task_display_name": "Language Grounding", "research_name": "Language-to-Moment Grounding", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "retrieval", "architecture_family": "retrieval ranker", "primary_direction": "C. Egocentric Vision & Interaction", "input": "Caption/object/interaction query features and a set of candidate sensor-window features.", "input_short": "text-like query and candidate windows", "process": "query features -> candidate index -> cosine ranker", "output": "A ranked list of windows, with the correct matching window ideally near rank 1.", "output_short": "ranked matching moments", "metric_key": "mrr", "metric_name": "MRR", "metric_direction": "higher", "minimal_primary_metric": 0.016023479050338015, "neural_primary_metric": 0.01684125567132316, "counts": { "num_queries": 348, "num_train_windows": 813, "num_test_windows": 348 }, "meaning": "Retrieve the matching time window for an annotation-derived text query.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/caption_grounding.md", "minimal_metrics": "results/episode_task_suite/caption_grounding/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json" }, "task_number": 8, "suite_label": "Task 08" }, { "task_id": "cross_modal_retrieval", "task_display_name": "Cross-Modal Retrieval", "research_name": "Multimodal Representation Retrieval", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "retrieval", "architecture_family": "two-tower retrieval head", "primary_direction": "D. Scene Reconstruction & World Modeling", "input": "Query side: motion, IMU, and camera/pose features. Candidate side: depth and video features.", "input_short": "motion/IMU/pose query; depth/video candidates", "process": "modality split -> projection -> nearest-neighbor ranker", "output": "A ranked list of candidate depth/video windows.", "output_short": "ranked visual windows", "metric_key": "mrr", "metric_name": "MRR", "metric_direction": "higher", "minimal_primary_metric": 0.26925966892956127, "neural_primary_metric": 0.1299971898648288, "counts": { "num_queries": 348, "num_train_windows": 813, "num_test_windows": 348 }, "meaning": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/cross_modal_retrieval.md", "minimal_metrics": "results/episode_task_suite/cross_modal_retrieval/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json" }, "task_number": 9, "suite_label": "Task 09" }, { "task_id": "modality_reconstruction", "task_display_name": "Cross-Modal Reconstruction", "research_name": "Modality Feature Reconstruction", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "forecast", "architecture_family": "feature regressor", "primary_direction": "B. 3D/4D Reconstruction & Neural Rendering", "input": "Motion, IMU, and camera/pose features as input; depth/video features as the regression target.", "input_short": "motion, IMU, and camera/pose features", "process": "source-target split -> scaler -> regression head", "output": "A reconstructed depth/video feature vector.", "output_short": "reconstructed depth/video vector", "metric_key": "r2", "metric_name": "R2", "metric_direction": "higher", "minimal_primary_metric": -0.015271898913936655, "neural_primary_metric": -0.010171410134180991, "counts": { "num_train_windows": 813, "num_test_windows": 348 }, "meaning": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/modality_reconstruction.md", "minimal_metrics": "results/episode_task_suite/modality_reconstruction/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json" }, "task_number": 10, "suite_label": "Task 10" }, { "task_id": "temporal_order", "task_display_name": "Temporal Order Verification", "research_name": "Temporal Order Verification", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "diagnostic", "architecture_family": "pairwise classifier", "primary_direction": "D. Scene Reconstruction & World Modeling", "input": "A pair of adjacent window vectors, plus their difference vector.", "input_short": "two adjacent windows plus difference vector", "process": "pair builder -> feature combiner -> binary classifier", "output": "A binary label: correct order or reversed order.", "output_short": "correct or reversed", "metric_key": "f1", "metric_name": "F1", "metric_direction": "higher", "minimal_primary_metric": 0.5399515738498789, "neural_primary_metric": 0.8520179372197308, "counts": { "num_samples": 2320, "num_train_samples": 1624, "num_test_samples": 696 }, "meaning": "Tell whether two neighboring windows are in chronological order or reversed.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/temporal_order.md", "minimal_metrics": "results/episode_task_suite/temporal_order/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json" }, "task_number": 11, "suite_label": "Task 11" }, { "task_id": "misalignment_detection", "task_display_name": "Multimodal Synchronization Detection", "research_name": "Cross-Modal Misalignment Detection", "provenance_source": "walkthrough_backed_task_contract", "origin_count_label": "unified task", "family": "diagnostic", "architecture_family": "pairwise classifier", "primary_direction": "B. 3D/4D Reconstruction & Neural Rendering", "input": "A motion-side feature group and a visual/depth-side feature group, either aligned or artificially shifted.", "input_short": "motion-side and visual/depth-side feature groups", "process": "aligned/shifted pairs -> feature combiner -> binary classifier", "output": "A binary label: aligned or shifted.", "output_short": "aligned or shifted", "metric_key": "f1", "metric_name": "F1", "metric_direction": "higher", "minimal_primary_metric": 0.5051698670605613, "neural_primary_metric": 0.7152682255845944, "counts": { "num_samples": 2306, "num_train_samples": 1614, "num_test_samples": 692 }, "meaning": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.", "artifact_sources": { "walkthrough": "results/episode_task_suite/task_walkthroughs/misalignment_detection.md", "minimal_metrics": "results/episode_task_suite/misalignment_detection/metrics.json", "neural_metrics": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json" }, "task_number": 12, "suite_label": "Task 12" }, { "task_id": "long_horizon_next_action", "task_display_name": "Long-Horizon Next-Action Forecasting", "research_name": "Long-Horizon Next-Action Forecasting", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "classification", "architecture_family": "minimal_softmax", "primary_direction": "sample-supported extension", "input": "Current 20-frame non-caption multimodal window.", "input_short": "Current 20-frame non-caption multimodal window.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "Action label five seconds later.", "output_short": "Action label five seconds later.", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.07499999999999998, "neural_primary_metric": 0.06545454545454546, "counts": { "num_windows": 1073, "num_eval_windows": 322, "num_train_windows": 751, "num_test_windows": 322, "num_classes": 18 }, "meaning": "Tests whether the current state carries enough procedure context to forecast beyond the one-second core next-action task.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json" }, "task_number": 13, "suite_label": "Task 13" }, { "task_id": "next_subtask_forecast", "task_display_name": "Long-Horizon Next-Subtask Forecasting", "research_name": "Long-Horizon Next-Subtask Forecasting", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "classification", "architecture_family": "minimal_softmax", "primary_direction": "sample-supported extension", "input": "Current 20-frame non-caption multimodal window.", "input_short": "Current 20-frame non-caption multimodal window.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "Procedure subtask label five seconds later.", "output_short": "Procedure subtask label five seconds later.", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.04545454545454545, "neural_primary_metric": 0.050724637681159424, "counts": { "num_windows": 1141, "num_eval_windows": 342, "num_train_windows": 799, "num_test_windows": 342, "num_classes": 14 }, "meaning": "Moves from immediate action anticipation to higher-level procedure-state prediction.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json" }, "task_number": 14, "suite_label": "Task 14" }, { "task_id": "interaction_text_prediction", "task_display_name": "Interaction Text Prediction", "research_name": "Interaction Text Prediction", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "classification", "architecture_family": "minimal_softmax", "primary_direction": "sample-supported extension", "input": "Current 20-frame sensor window with caption-text features removed.", "input_short": "Current 20-frame sensor window with caption-text features removed.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "Raw annotation interaction phrase for the same window.", "output_short": "Raw annotation interaction phrase for the same window.", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.04444444444444444, "neural_primary_metric": 0.0380952380952381, "counts": { "num_windows": 192, "num_eval_windows": 58, "num_train_windows": 134, "num_test_windows": 58, "num_classes": 46 }, "meaning": "Uses the raw caption JSON interaction field as a language target instead of only the hashed text feature.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json" }, "task_number": 15, "suite_label": "Task 15" }, { "task_id": "action_object_relation", "task_display_name": "Action-Object Relation Prediction", "research_name": "Action-Object Relation Prediction", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "classification", "architecture_family": "minimal_softmax", "primary_direction": "sample-supported extension", "input": "Current 20-frame sensor window with caption-text features removed.", "input_short": "Current 20-frame sensor window with caption-text features removed.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "Joint action plus active object-set relation.", "output_short": "Joint action plus active object-set relation.", "metric_key": "macro_f1", "metric_name": "macro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.0, "neural_primary_metric": 0.0, "counts": { "num_windows": 178, "num_eval_windows": 53, "num_train_windows": 125, "num_test_windows": 53, "num_classes": 42 }, "meaning": "Evaluates whether a model can bind what action is happening to which objects are involved.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json" }, "task_number": 16, "suite_label": "Task 16" }, { "task_id": "object_set_forecast", "task_display_name": "Future Object-Set Forecasting", "research_name": "Future Object-Set Forecasting", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "multi_label", "architecture_family": "minimal_ridge_multilabel", "primary_direction": "sample-supported extension", "input": "Current 20-frame sensor window with caption-text features removed.", "input_short": "Current 20-frame sensor window with caption-text features removed.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "Object set active five seconds later.", "output_short": "Object set active five seconds later.", "metric_key": "micro_f1", "metric_name": "micro-F1", "metric_direction": "higher", "minimal_primary_metric": 0.16939890710382516, "neural_primary_metric": 0.19718309859154928, "counts": { "num_windows": 188, "num_train_windows": 132, "num_test_windows": 56 }, "meaning": "Predicts which objects will become relevant soon, not only which objects are relevant now.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json" }, "task_number": 17, "suite_label": "Task 17" }, { "task_id": "imu_to_hand_pose", "task_display_name": "IMU-to-Hand Pose Reconstruction", "research_name": "IMU-to-Hand Pose Reconstruction", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "regression", "architecture_family": "minimal_ridge_regression", "primary_direction": "sample-supported extension", "input": "Current IMU acceleration/gyroscope feature block only.", "input_short": "Current IMU acceleration/gyroscope feature block only.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "Current left/right hand joint feature blocks.", "output_short": "Current left/right hand joint feature blocks.", "metric_key": "mae", "metric_name": "MAE", "metric_direction": "lower", "minimal_primary_metric": 0.042049407958984375, "neural_primary_metric": 0.042562149465084076, "counts": { "num_windows": 1161, "num_train_windows": 813, "num_test_windows": 348 }, "meaning": "A sensor-bridge probe for how much hand configuration can be recovered from inertial motion alone.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json" }, "task_number": 18, "suite_label": "Task 18" }, { "task_id": "camera_view_sync_retrieval", "task_display_name": "Camera-View Synchronization Retrieval", "research_name": "Camera-View Synchronization Retrieval", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "retrieval", "architecture_family": "minimal_ridge_projection_cosine_retrieval", "primary_direction": "sample-supported extension", "input": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.", "input_short": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "The synchronized held-out camera-3 window.", "output_short": "The synchronized held-out camera-3 window.", "metric_key": "mrr", "metric_name": "MRR", "metric_direction": "higher", "minimal_primary_metric": 0.4943004846572876, "neural_primary_metric": 0.24086658656597137, "counts": { "num_train_windows": 813, "num_test_windows": 348 }, "meaning": "Stress-tests multi-camera time alignment beyond the core cross-modal retrieval task.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json" }, "task_number": 19, "suite_label": "Task 19" }, { "task_id": "time_to_transition", "task_display_name": "Time-to-Next-Transition Regression", "research_name": "Time-to-Next-Transition Regression", "provenance_source": "historical_result_bundle", "origin_count_label": "unified task", "family": "regression", "architecture_family": "minimal_ridge_regression", "primary_direction": "sample-supported extension", "input": "Current 20-frame non-caption multimodal window.", "input_short": "Current 20-frame non-caption multimodal window.", "process": "shared window features -> task-specific target builder -> minimal/neural head", "output": "Frames until the next action-label boundary, capped at 200 frames.", "output_short": "Frames until the next action-label boundary, capped at 200 frames.", "metric_key": "mae", "metric_name": "MAE frames", "metric_direction": "lower", "minimal_primary_metric": 10.53735637664795, "neural_primary_metric": 10.55449390411377, "counts": { "num_windows": 1161, "num_train_windows": 813, "num_test_windows": 348 }, "meaning": "Turns boundary detection into a continuous timing estimate for procedural control.", "artifact_sources": { "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/", "minimal_metrics": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json", "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json" }, "task_number": 20, "suite_label": "Task 20" } ] }