docs/data/glossary.json

{
  "title": "Ropedia Xperience-10M Glossary",
  "status": "published",
  "purpose": "Define reader-facing project terms and adjacent technical field terms for embodied AI, egocentric multimodal data, spatial intelligence, world models, VLA/policy learning, evaluation, and public artifact reading.",
  "categories": [
    {
      "id": "dataset_scope",
      "label": "Dataset and scope",
      "description": "Public data boundaries, evidence lines, and how each result family should be read."
    },
    {
      "id": "files_features",
      "label": "Files and features",
      "description": "Raw sample files, windows, feature manifests, and public-safe derivatives."
    },
    {
      "id": "multimodal_sensing",
      "label": "Multimodal sensing",
      "description": "Video, audio, depth, IMU, motion capture, calibration, and synchronization terms."
    },
    {
      "id": "spatial_geometry",
      "label": "Spatial geometry",
      "description": "Camera pose, SLAM, coordinate frames, point clouds, 3D reconstruction, and spatial grounding."
    },
    {
      "id": "temporal_world_models",
      "label": "Temporal and world models",
      "description": "Future prediction, rollouts, forward dynamics, long-horizon forecasting, and temporal leakage."
    },
    {
      "id": "robotics_vla",
      "label": "Robotics and VLA",
      "description": "Vision-language-action, policies, action chunks, imitation learning, contact, and dexterity."
    },
    {
      "id": "tasks_metrics",
      "label": "Tasks and metrics",
      "description": "Task contracts, scored records, direct scores, compact proxies, and audits."
    },
    {
      "id": "training_eval",
      "label": "Training and evaluation",
      "description": "Splits, held-out evaluation, metric types, prompt/schema checks, adapters, and distributed training."
    },
    {
      "id": "models_runs",
      "label": "Models and runs",
      "description": "Baseline families, Qwen3-Omni, Cosmos3, LoRA adapters, and full-parameter gates."
    },
    {
      "id": "public_surfaces",
      "label": "Public surfaces",
      "description": "GitHub, website, Hugging Face repos, parity checks, and package validation."
    }
  ],
  "entries": [
    {
      "term": "Evidence line",
      "category": "dataset_scope",
      "plain_meaning": "A reading lane for a group of results.",
      "project_usage": "Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.",
      "do_not_confuse_with": "Qwen run versions v1-v6, which are model-run lineage.",
      "primary_files": [
        "TWO_EVIDENCE_LINES.md",
        "docs/data/two_evidence_lines.json"
      ]
    },
    {
      "term": "Official gated data",
      "category": "dataset_scope",
      "plain_meaning": "Upstream files that require official dataset access.",
      "project_usage": "Raw Xperience-10M MP4/HDF5/RRD files and full source directories remain outside the public repo.",
      "do_not_confuse_with": "Public-safe metrics, derived features, figures, and manifests.",
      "primary_files": [
        "DATA_NOTICE.md",
        "REPRODUCIBILITY.md"
      ]
    },
    {
      "term": "Public sample episode",
      "category": "dataset_scope",
      "plain_meaning": "One officially available sample episode.",
      "project_usage": "The fully inspectable Line 1 unit used for raw-file browsing, 20-frame windows, task construction, and single-episode baselines.",
      "do_not_confuse_with": "The selected-128 comparison rows.",
      "primary_files": [
        "docs/data/raw_sample_files.json",
        "docs/single_episode_explorer.html"
      ]
    },
    {
      "term": "Selected 128 episodes",
      "category": "dataset_scope",
      "plain_meaning": "A public-safe selected subset of official gated episode paths.",
      "project_usage": "Line 2 uses derived windows/features and keeps links back to official episode ids and gated source paths.",
      "do_not_confuse_with": "Redistributed raw MP4/HDF5/RRD data.",
      "primary_files": [
        "XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md",
        "docs/data/xperience10m_128_episode_feature_index.json"
      ]
    },
    {
      "term": "Xperience-10M",
      "category": "dataset_scope",
      "plain_meaning": "The upstream embodied human-interaction dataset.",
      "project_usage": "Source dataset behind the public sample, selected-128 features, task suite, and model diagnostics.",
      "do_not_confuse_with": "This repo, which only redistributes public-safe derived artifacts.",
      "primary_files": [
        "XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
        "docs/data/xperience10m_dataset_card_alignment.json"
      ]
    },
    {
      "term": "20-frame window",
      "category": "files_features",
      "plain_meaning": "A fixed short clip slice.",
      "project_usage": "The sample episode is converted into aligned 20-frame units for features, labels, and many task heads.",
      "do_not_confuse_with": "A full episode or arbitrary video segment.",
      "primary_files": [
        "results/episode_task_suite/windows.csv",
        "EVALUATION_PROTOCOL.md"
      ]
    },
    {
      "term": "annotation.hdf5",
      "category": "files_features",
      "plain_meaning": "Upstream annotation container for the sample.",
      "project_usage": "Contains original labels/metadata; some public derived files expose processed features instead of every raw text field.",
      "do_not_confuse_with": "Task result summaries.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "Episode",
      "category": "files_features",
      "plain_meaning": "One recorded interaction sequence.",
      "project_usage": "The basic source unit behind windows, labels, and train/val/test splits.",
      "do_not_confuse_with": "A 20-frame window.",
      "primary_files": [
        "docs/data/raw_sample_files.json",
        "docs/data/xperience10m_128_episode_feature_index.json"
      ]
    },
    {
      "term": "Feature manifest",
      "category": "files_features",
      "plain_meaning": "A map from model-input columns to source modalities.",
      "project_usage": "Explains feature groups and dimensions for the sample task suite.",
      "do_not_confuse_with": "The raw annotation file.",
      "primary_files": [
        "results/episode_task_suite/feature_manifest.json"
      ]
    },
    {
      "term": "Interaction text",
      "category": "files_features",
      "plain_meaning": "Natural-language interaction/caption content.",
      "project_usage": "Used by task 15 and some derived text features; public matrices record direct or compact-proxy status.",
      "do_not_confuse_with": "Numeric action ids or subtask ids.",
      "primary_files": [
        "TASK_SUITE_20.md",
        "docs/data/task_method_20_result_matrix.json"
      ]
    },
    {
      "term": "Modality",
      "category": "files_features",
      "plain_meaning": "A type of signal.",
      "project_usage": "Video, audio, depth, pose/SLAM, motion capture, inertial, calibration, and language-derived signals.",
      "do_not_confuse_with": "A task target.",
      "primary_files": [
        "docs/data/modality_atlas.json",
        "results/episode_task_suite/feature_manifest.json"
      ]
    },
    {
      "term": "Raw sample file map",
      "category": "files_features",
      "plain_meaning": "A human-readable inventory of the sample episode files.",
      "project_usage": "Explains videos, annotations, calibration, motion, and derived previews.",
      "do_not_confuse_with": "A training manifest.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "visualization.rrd",
      "category": "files_features",
      "plain_meaning": "Rerun viewer recording for visual inspection.",
      "project_usage": "Can be downloaded from the official sample dataset and opened in Rerun 0.29.0 to inspect the sample episode. It is not used for published training or metric rows.",
      "do_not_confuse_with": "MP4 video streams or model inputs.",
      "primary_files": [
        "docs/data/raw_sample_files.json",
        "REPRODUCIBILITY.md"
      ]
    },
    {
      "term": "Window stride",
      "category": "files_features",
      "plain_meaning": "The frame step between neighboring windows.",
      "project_usage": "Creates overlapping examples while preserving chronological order and leakage controls.",
      "do_not_confuse_with": "Video frame rate.",
      "primary_files": [
        "EVALUATION_PROTOCOL.md"
      ]
    },
    {
      "term": "Audio waveform",
      "category": "multimodal_sensing",
      "plain_meaning": "A time-series pressure signal from sound.",
      "project_usage": "The audio ablation measures whether embedded audio helps selected task contracts.",
      "do_not_confuse_with": "Language captions or text labels.",
      "primary_files": [
        "docs/data/audio_ablation_summary.json"
      ]
    },
    {
      "term": "Calibration",
      "category": "multimodal_sensing",
      "plain_meaning": "Parameters that relate sensors to each other and to physical space.",
      "project_usage": "Needed to interpret camera streams, depth, pose, and synchronized multimodal features together.",
      "do_not_confuse_with": "A model training hyperparameter.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "Camera extrinsics",
      "category": "multimodal_sensing",
      "plain_meaning": "A camera position and orientation relative to another coordinate frame.",
      "project_usage": "Connects different camera streams and world coordinates.",
      "do_not_confuse_with": "Camera intrinsics.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "Camera intrinsics",
      "category": "multimodal_sensing",
      "plain_meaning": "Internal camera parameters such as focal length and distortion.",
      "project_usage": "Explain how image pixels project to rays for geometry tasks.",
      "do_not_confuse_with": "Camera extrinsics.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "Depth map",
      "category": "multimodal_sensing",
      "plain_meaning": "A per-pixel estimate of distance from the camera.",
      "project_usage": "Depth-derived signals support spatial and geometry-oriented tasks.",
      "do_not_confuse_with": "RGB brightness or semantic segmentation.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "Egocentric video",
      "category": "multimodal_sensing",
      "plain_meaning": "Video captured from a first-person or body-mounted viewpoint.",
      "project_usage": "The sample streams are egocentric views of human interaction and are the visual basis for many tasks.",
      "do_not_confuse_with": "Third-person robot-camera footage.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "Fisheye camera",
      "category": "multimodal_sensing",
      "plain_meaning": "A wide-angle camera with strong lens distortion.",
      "project_usage": "Multiple fisheye MP4 streams give broad room coverage but need calibration-aware interpretation.",
      "do_not_confuse_with": "A rectilinear pinhole camera image.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "IMU",
      "category": "multimodal_sensing",
      "plain_meaning": "An inertial measurement unit with accelerometer and gyroscope signals.",
      "project_usage": "Supports motion, temporal, and sensor-bridging tasks.",
      "do_not_confuse_with": "Motion capture skeleton data.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "Metric depth",
      "category": "multimodal_sensing",
      "plain_meaning": "Depth expressed in physical units rather than arbitrary relative scale.",
      "project_usage": "Useful for distance-sensitive spatial reasoning and reconstruction targets.",
      "do_not_confuse_with": "Relative monocular depth.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "Motion capture",
      "category": "multimodal_sensing",
      "plain_meaning": "A system that records body or hand motion over time.",
      "project_usage": "Provides hand/body motion evidence when exposed through public-safe derived features.",
      "do_not_confuse_with": "Video-only pose estimation.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "RGB frame",
      "category": "multimodal_sensing",
      "plain_meaning": "A color image frame from a video stream.",
      "project_usage": "Used for visual statistics, previews, and many model inputs.",
      "do_not_confuse_with": "Depth values or point-cloud coordinates.",
      "primary_files": [
        "results/episode_task_suite/feature_manifest.json"
      ]
    },
    {
      "term": "Sensor alignment",
      "category": "multimodal_sensing",
      "plain_meaning": "Putting different sensor streams into a shared temporal or spatial reference.",
      "project_usage": "Used to make video, audio, pose, depth, IMU, and mocap usable in the same task input.",
      "do_not_confuse_with": "Model ensembling.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "Stereo camera",
      "category": "multimodal_sensing",
      "plain_meaning": "A paired-camera setup that supports depth or geometry estimation.",
      "project_usage": "The sample browser exposes stereo streams as part of the visual modality set.",
      "do_not_confuse_with": "Single-view RGB video.",
      "primary_files": [
        "docs/data/raw_sample_files.json"
      ]
    },
    {
      "term": "Timestamp synchronization",
      "category": "multimodal_sensing",
      "plain_meaning": "Aligning sensor samples by time.",
      "project_usage": "The task suite assumes aligned windows across modalities so labels and features refer to the same moment.",
      "do_not_confuse_with": "Randomly joining files with similar names.",
      "primary_files": [
        "EVALUATION_PROTOCOL.md"
      ]
    },
    {
      "term": "3D reconstruction",
      "category": "spatial_geometry",
      "plain_meaning": "Recovering 3D scene structure from sensor data.",
      "project_usage": "One core spatial-intelligence direction for Xperience-style data.",
      "do_not_confuse_with": "Next-action classification.",
      "primary_files": [
        "docs/data/three_foundation_pipelines.json"
      ]
    },
    {
      "term": "Affordance",
      "category": "spatial_geometry",
      "plain_meaning": "An action possibility offered by an object or scene.",
      "project_usage": "Relevant when moving from observed human interaction to robot-action or VLA tasks.",
      "do_not_confuse_with": "A detected object category alone.",
      "primary_files": [
        "docs/data/three_foundation_pipelines.json"
      ]
    },
    {
      "term": "Camera pose",
      "category": "spatial_geometry",
      "plain_meaning": "The camera position and orientation at a time step.",
      "project_usage": "Supports spatial-intelligence tasks, view synchronization, and geometry diagnostics.",
      "do_not_confuse_with": "The human body pose.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "Coordinate frame",
      "category": "spatial_geometry",
      "plain_meaning": "A reference system for positions and orientations.",
      "project_usage": "Needed when comparing camera, body, object, and world measurements.",
      "do_not_confuse_with": "A video frame.",
      "primary_files": [
        "EVALUATION_PROTOCOL.md"
      ]
    },
    {
      "term": "Object-centric representation",
      "category": "spatial_geometry",
      "plain_meaning": "A representation organized around objects and their relations.",
      "project_usage": "Useful for object relevance, object-set forecast, and action-object relation tasks.",
      "do_not_confuse_with": "A flat feature vector without object identity.",
      "primary_files": [
        "docs/data/task_suite_20.json"
      ]
    },
    {
      "term": "Odometry",
      "category": "spatial_geometry",
      "plain_meaning": "Motion estimated from sensor changes over time.",
      "project_usage": "A relevant spatial term for ego-motion and camera-pose reasoning.",
      "do_not_confuse_with": "Ground-truth motion capture.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "Point cloud",
      "category": "spatial_geometry",
      "plain_meaning": "A set of 3D points representing scene structure.",
      "project_usage": "A likely target or intermediate representation for spatial-intelligence extensions.",
      "do_not_confuse_with": "A 2D image grid.",
      "primary_files": [
        "docs/data/three_foundation_pipelines.json"
      ]
    },
    {
      "term": "SLAM",
      "category": "spatial_geometry",
      "plain_meaning": "Simultaneous localization and mapping.",
      "project_usage": "A field term for estimating camera motion and scene structure from sensor observations.",
      "do_not_confuse_with": "A task label or action class.",
      "primary_files": [
        "docs/data/modality_atlas.json"
      ]
    },
    {
      "term": "Spatial grounding",
      "category": "spatial_geometry",
      "plain_meaning": "Linking language or labels to locations, objects, or geometry.",
      "project_usage": "Connects language grounding tasks with 3D/spatial reasoning.",
      "do_not_confuse_with": "General text classification.",
      "primary_files": [
        "docs/data/research_directions.json"
      ]
    },
    {
      "term": "Trajectory",
      "category": "spatial_geometry",
      "plain_meaning": "A sequence of positions over time.",
      "project_usage": "Used for hand motion, camera motion, and future-path tasks.",
      "do_not_confuse_with": "A single coordinate or label.",
      "primary_files": [
        "TASK_SUITE_20.md"
      ]
    },
    {
      "term": "Action forecasting",
      "category": "temporal_world_models",
      "plain_meaning": "Predicting a future action before it happens.",
      "project_usage": "Covered by next-action and long-horizon task contracts.",
      "do_not_confuse_with": "Recognizing the current action only.",
      "primary_files": [
        "docs/data/task_suite_20.json"
      ]
    },
    {
      "term": "Autoregressive prediction",
      "category": "temporal_world_models",
      "plain_meaning": "Generating each future token, state, or frame conditioned on prior outputs.",
      "project_usage": "Relevant for model branches that produce structured JSON or temporal predictions.",
      "do_not_confuse_with": "A one-shot classifier.",
      "primary_files": [
        "docs/data/foundation_model_plan.json"
      ]
    },
    {
      "term": "Forward dynamics",
      "category": "temporal_world_models",
      "plain_meaning": "Predicting the next state from the current state and action/context.",
      "project_usage": "The Cosmos3-Super LoRA branch uses a forward-dynamics-style diagnostic contract.",
      "do_not_confuse_with": "Reverse inference from result back to cause.",
      "primary_files": [
        "docs/data/omni_model_comparison.json"
      ]
    },
    {
      "term": "Latent state",
      "category": "temporal_world_models",
      "plain_meaning": "A hidden representation that summarizes observed context.",
      "project_usage": "Useful for future foundation-model and world-model training plans.",
      "do_not_confuse_with": "A visible annotation column.",
      "primary_files": [
        "docs/data/foundation_model_plan.json"
      ]
    },
    {
      "term": "Long-horizon prediction",
      "category": "temporal_world_models",
      "plain_meaning": "Predicting outcomes several seconds or steps ahead.",
      "project_usage": "Tasks 13 and 14 test longer temporal context beyond immediate recognition.",
      "do_not_confuse_with": "Single-frame classification.",
      "primary_files": [
        "docs/data/task_suite_20.json"
      ]
    },
    {
      "term": "Next-frame prediction",
      "category": "temporal_world_models",
      "plain_meaning": "Predicting future visual frames from past frames.",
      "project_usage": "A field-level world-model objective related to the human-video world-model direction.",
      "do_not_confuse_with": "Next-action prediction.",
      "primary_files": [
        "docs/data/three_foundation_pipelines.json"
      ]
    },
    {
      "term": "Object persistence",
      "category": "temporal_world_models",
      "plain_meaning": "Tracking that an object remains present over time even when view or interaction changes.",
      "project_usage": "Relevant for object-set forecast and long-video reasoning.",
      "do_not_confuse_with": "A single-frame object detection.",
      "primary_files": [
        "docs/data/task_suite_20.json"
      ]
    },
    {
      "term": "Rollout",
      "category": "temporal_world_models",
      "plain_meaning": "Repeatedly predicting future steps from a model state.",
      "project_usage": "Important for judging world models beyond one-step prediction.",
      "do_not_confuse_with": "A held-out static test row.",
      "primary_files": [
        "docs/data/three_foundation_pipelines.json"
      ]
    },
    {
      "term": "Subtask forecasting",
      "category": "temporal_world_models",
      "plain_meaning": "Predicting the next higher-level step in an activity.",
      "project_usage": "Used in the future-task probe line for Qwen3-Omni.",
      "do_not_confuse_with": "Frame-level action classification.",
      "primary_files": [
        "docs/data/task_method_20_result_matrix.json"
      ]
    },
    {
      "term": "Teacher forcing",
      "category": "temporal_world_models",
      "plain_meaning": "Training a sequence model using ground-truth previous outputs.",
      "project_usage": "A likely training option for future sequence/world-model baselines.",
      "do_not_confuse_with": "Free-running rollout evaluation.",
      "primary_files": [
        "docs/data/foundation_model_plan.json"
      ]
    },
    {
      "term": "Temporal leakage",
      "category": "temporal_world_models",
      "plain_meaning": "Using future information that would not be available at prediction time.",
      "project_usage": "Avoided by chronological splits and target-side feature controls.",
      "do_not_confuse_with": "A low model score.",
      "primary_files": [
        "EVALUATION_PROTOCOL.md"
      ]
    },
    {
      "term": "Transition timing",
      "category": "temporal_world_models",
      "plain_meaning": "Estimating when the next state or action transition happens.",
      "project_usage": "Task 20 turns temporal change into a regression target.",
      "do_not_confuse_with": "Classifying the transition type only.",
      "primary_files": [
        "docs/data/task_suite_20.json"
      ]
    },
    {
      "term": "Action chunk",
      "category": "robotics_vla",
      "plain_meaning": "A short sequence of low-level actions predicted together.",
      "project_usage": "The VLA figure and plan use action chunks as the policy-output concept.",
      "do_not_confuse_with": "A natural-language action label.",
      "primary_files": [
        "docs/data/three_foundation_pipelines.json"
      ]
    },
    {
      "term": "Behavior cloning",
      "category": "robotics_vla",
      "plain_meaning": "A supervised imitation-learning method for predicting demonstrated actions.",
      "project_usage": "A plausible baseline once action targets are converted.",
      "do_not_confuse_with": "Generative video modeling.",
      "primary_files": [
        "docs/data/foundation_model_plan.json"
      ]
    },
    {
      "term": "Contact event",
      "category": "robotics_vla",
      "plain_meaning": "A moment when a hand, body, or tool touches an object or surface.",
      "project_usage": "Used in contact-related tasks and action-quality interpretation.",
      "do_not_confuse_with": "Visual co-occurrence without touch.",
      "primary_files": [
        "docs/data/task_suite_20.json"
      ]
    },
    {
      "term": "Dexterity",
      "category": "robotics_vla",
      "plain_meaning": "Fine-grained physical manipulation ability.",
      "project_usage": "Relevant to hand-object interaction, contact, and VLA/policy directions.",
      "do_not_confuse_with": "High text-generation accuracy.",
      "primary_files": [
        "docs/data/research_directions.json"
      ]
    },
    {
      "term": "End effector",
      "category": "robotics_vla",
      "plain_meaning": "The robot part that acts on the world, such as a gripper or hand.",
      "project_usage": "A key target frame for future manipulation-policy conversion.",
      "do_not_confuse_with": "A camera or global scene coordinate.",
      "primary_files": [
        "docs/data/three_foundation_pipelines.json"
      ]
    },
    {
      "term": "Hand-object interaction",
      "category": "robotics_vla",
      "plain_meaning": "A physical interaction between hands and objects.",
      "project_usage": "A central signal family behind action, contact, object relevance, and interaction-text tasks.",
      "do_not_confuse_with": "Object detection without action.",
      "primary_files": [
        "docs/data/task_suite_20.json"
      ]
    },
    {
      "term": "Imitation learning",
      "category": "robotics_vla",
      "plain_meaning": "Training a policy to imitate demonstrated behavior.",
      "project_usage": "Relevant when converting human video/motion into action supervision.",
      "do_not_confuse_with": "Reinforcement learning from online robot trials.",
      "primary_files": [
        "docs/data/foundation_model_plan.json"
      ]
    },
    {
      "term": "Language grounding",
      "category": "robotics_vla",
      "plain_meaning": "Connecting text to observed objects, actions, or spatial context.",
      "project_usage": "Task 8 and VLA directions use language as grounded supervision rather than standalone text.",
      "do_not_confuse_with": "Caption fluency alone.",
      "primary_files": [
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