Datasets:

cy0307
/

ropedia-xperience-10m-task-suite-artifacts

# Junior-Friendly 12-Task Walkthroughs

This file explains every task in the Xperience-10M episode suite as an input -> process -> output pipeline.
It is generated by `scripts/task_walkthroughs.py` from committed metrics plus hand-curated task explanations.

## Shared Pipeline

- Read annotation.hdf5 and synchronized video-derived features.
- Slice the episode into 20-frame windows with stride 5.
- Build a 8,546-dimensional aligned feature vector from the synchronized modality groups.
- Construct a task-specific target from labels, future frames, paired windows, or modality splits.
- Train a minimal head and, when enabled, a neural MLP head.
- Write metrics, predictions, and model artifacts for downstream exploration.

## Task Walkthroughs

### Action Recognition (`timeline_action`)

**Research name:** Egocentric Action Recognition

**Family:** supervised; multiclass classifier; C. Egocentric Vision & Interaction.

**Goal:** Look at one short multimodal window and name what action is happening now.

**Case study:** In the coffee-making sample, if the 20-frame window is during a pouring moment, the task asks the model to output an action such as Pour coffee or Pour milk into coffee.

**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.

**Middle process modules:**
- Window builder slices the episode into short overlapping windows.
- Feature assembler concatenates all current feature blocks.
- Label builder reads the action annotation for the center of the window.
- Classifier head maps the window vector to one action class.
- Evaluator compares predicted action labels against the held-out chronological segment.

**Output:** A single action class for the current window.

**Metric:** macro-F1 (higher is better). Minimal `0.0500`, neural MLP `0.0148`.

**Junior mental model:** This is like asking: given this tiny movie clip plus sensor readings, what is the person doing right now?

**Current limitation:** The one-episode chronological split contains future action classes that were not present in training, so low test macro-F1 is expected.

### Procedure Step Recognition (`timeline_subtask`)

**Research name:** Temporal Subtask Recognition

**Family:** supervised; multiclass classifier; C. Egocentric Vision & Interaction.

**Goal:** Predict the higher-level task stage for the current window.

**Case study:** A pouring action may belong to a broader subtask such as preparing or pouring a drink. The model predicts that broader stage instead of a fine action.

**Input:** The same all-modality window vector used by action recognition.

**Middle process modules:**
- Window builder creates the current temporal slice.
- Feature assembler keeps all available modality blocks.
- Subtask label builder maps the current timestamp to a subtask annotation.
- Classifier head predicts the subtask class.
- Evaluator reports class-balanced scores so rare subtasks matter.

**Output:** A single subtask label for the current window.

**Metric:** macro-F1 (higher is better). Minimal `0.0506`, neural MLP `0.0281`.

**Junior mental model:** Action is the verb; subtask is the chapter of the activity.

**Current limitation:** Single-episode ordering means some later subtasks appear only in test, so this is a pipeline check rather than a general benchmark.

### Action Boundary Detection (`transition_detection`)

**Research name:** Temporal Action Segmentation

**Family:** diagnostic; binary classifier; C. Egocentric Vision & Interaction.

**Goal:** Detect whether the current window is near a boundary between actions.

**Case study:** When the demonstrator changes from preparing to pouring, the model should flag a boundary instead of a steady action window.

**Input:** One all-modality window vector plus labels derived from action-change timestamps.

**Middle process modules:**
- Boundary builder scans action labels over time and marks windows near a change.
- Feature assembler supplies all current modality features.
- Binary classifier predicts steady vs boundary.
- Boundary matcher checks whether predicted boundary times are close to true boundary times.
- Evaluator reports macro-F1 and timing error, not just accuracy.

**Output:** A binary label: boundary or steady.

**Metric:** macro-F1 (higher is better). Minimal `0.6118`, neural MLP `0.5862`.

**Junior mental model:** This is the model's way of saying: something just changed here.

**Current limitation:** Boundaries are rare, so high accuracy can be misleading if the model predicts steady too often.

### Next-Action Prediction (`next_action`)

**Research name:** Short-Horizon Intention Prediction

**Family:** supervised; future-label classifier; C. Egocentric Vision & Interaction.

**Goal:** Use the current window to guess the action that will happen shortly after it.

**Case study:** If a window shows the person preparing to pour, the target can be the action 20 frames later, such as the start of pouring.

**Input:** The current all-modality window vector at time t.

**Middle process modules:**
- Window builder picks a current time window.
- Future label builder shifts the action target by 20 frames.
- Feature assembler uses only current information, not future features.
- Classifier head predicts the future action class.
- Evaluator checks whether the future action label is correct.

**Output:** A single action class for t+20 frames.

**Metric:** macro-F1 (higher is better). Minimal `0.0593`, neural MLP `0.0419`.

**Junior mental model:** This is short-horizon intention prediction: what will the person do next?

**Current limitation:** The public sample has unseen future classes in the chronological test split, which makes this very hard with one episode.

### Hand Trajectory Forecasting (`hand_trajectory_forecast`)

**Research name:** 3D Hand Motion Forecasting

**Family:** forecast; continuous regressor; A. Human Modeling & Motion Understanding.

**Goal:** Predict where the hands will move over the next few frames.

**Case study:** When the hand is moving toward a cup or bottle, the model predicts the future 3D hand-joint path.

**Input:** The current all-modality window vector at time t.

**Middle process modules:**
- Window builder chooses the current sensor window.
- Target builder extracts future left/right hand 3D joints from motion capture.
- Regression head predicts a continuous trajectory, not a class label.
- Output reshaper interprets the vector as future frames and joints.
- Evaluator computes MPJPE, the average 3D joint-position error.

**Output:** A future trajectory vector for left and right hand joints.

**Metric:** MPJPE (lower is better). Minimal `0.8647`, neural MLP `0.1079`.

**Junior mental model:** Instead of naming an action, this task draws the next hand path in 3D.

**Current limitation:** It is still a window-level forecast, not a full policy or long-horizon motion generator.

### Contact State Prediction (`contact_prediction`)

**Research name:** Human-Object Contact Prediction

**Family:** supervised; binary classifier; A. Human Modeling & Motion Understanding.

**Goal:** Predict whether the body or hand is in contact with something.

**Case study:** During manipulation, the hand may touch a cup, table, or bottle. The task asks whether any contact is happening.

**Input:** Non-contact and non-caption feature blocks, so the answer is not directly leaked from the target labels.

**Middle process modules:**
- Feature selector removes contact-label and caption-label blocks.
- Target builder converts contact annotations into a binary label.
- Binary classifier predicts contact vs no contact.
- Evaluator reports macro-F1 and accuracy.
- Degeneracy checker records whether only one class appears.

**Output:** A binary contact label.

**Metric:** macro-F1 (higher is better). Minimal `1.0000`, neural MLP `1.0000`.

**Junior mental model:** This is a simple physical-interaction probe: is the person touching something now?

**Current limitation:** The current public sample is degenerate for this task because one class dominates, so perfect score does not mean the model learned contact physics.

### Object Relevance Prediction (`object_relevance`)

**Research name:** Object-Centric Interaction Recognition

**Family:** supervised; multi-label classifier; C. Egocentric Vision & Interaction.

**Goal:** Predict which objects matter in the current window.

**Case study:** If the person is pouring milk into coffee, relevant objects may include milk, cup, coffee, or container-like items.

**Input:** Non-caption feature blocks, so the model must infer objects from sensors rather than copying the caption words.

**Middle process modules:**
- Object vocabulary builder collects object labels from annotations.
- Feature selector removes caption-derived label blocks.
- Multi-label target builder creates a multi-hot object vector.
- Sigmoid heads predict each object's relevance independently.
- Evaluator reports micro-F1 and exact-match quality.

**Output:** A multi-label object set for the current window.

**Metric:** micro-F1 (higher is better). Minimal `0.1803`, neural MLP `0.1679`.

**Junior mental model:** A window can involve more than one object, so this is not a one-class classifier.

**Current limitation:** Object labels are sparse and language-derived, so this is currently a weak object-centric probe.

### Language Grounding (`caption_grounding`)

**Research name:** Language-to-Moment Grounding

**Family:** retrieval; retrieval ranker; C. Egocentric Vision & Interaction.

**Goal:** Given a text-like query from annotation, find the matching time window.

**Case study:** A query like Pour milk into coffee should rank the windows from the actual pouring moment higher than unrelated windows.

**Input:** Caption/object/interaction query features and a set of candidate sensor-window features.

**Middle process modules:**
- Query builder converts annotation words into a compact query representation.
- Candidate builder gathers held-out sensor windows.
- Projection head maps sensor windows into the query space.
- Ranker scores candidates by cosine similarity.
- Evaluator reports MRR and top-k retrieval accuracy.

**Output:** A ranked list of windows, with the correct matching window ideally near rank 1.

**Metric:** MRR (higher is better). Minimal `0.0160`, neural MLP `0.0168`.

**Junior mental model:** This is search: type a description, retrieve the matching moment.

**Current limitation:** Bag-of-objects text features are too simple for rich language grounding.

### Cross-Modal Retrieval (`cross_modal_retrieval`)

**Research name:** Multimodal Representation Retrieval

**Family:** retrieval; two-tower retrieval head; D. Scene Reconstruction & World Modeling.

**Goal:** Use one group of modalities to retrieve the matching window from another group.

**Case study:** Use motion, IMU, and camera-pose signals from a pouring moment to retrieve the matching depth/video representation for that same moment.

**Input:** Query side: motion, IMU, and camera/pose features. Candidate side: depth and video features.

**Middle process modules:**
- Feature splitter separates query modalities from target modalities.
- Projection head maps the query vector into target-modality space.
- Candidate index stores target vectors from held-out windows.
- Ranker retrieves nearest candidates by cosine similarity.
- Evaluator reports MRR, top-1, top-5, and top-10 accuracy.

**Output:** A ranked list of candidate depth/video windows.

**Metric:** MRR (higher is better). Minimal `0.2693`, neural MLP `0.1300`.

**Junior mental model:** This checks whether different sensors agree about the same moment in time.

**Current limitation:** Good retrieval means useful alignment signal, but it is not yet 3D reconstruction or rendering.

### Cross-Modal Reconstruction (`modality_reconstruction`)

**Research name:** Modality Feature Reconstruction

**Family:** forecast; feature regressor; B. 3D/4D Reconstruction & Neural Rendering.

**Goal:** Predict one modality feature block from other modality blocks.

**Case study:** Given motion, IMU, and camera-pose signals while the hand moves, predict the matching depth/video feature vector.

**Input:** Motion, IMU, and camera/pose features as input; depth/video features as the regression target.

**Middle process modules:**
- Feature splitter defines source and target modality blocks.
- Scaler normalizes source and target vectors using train statistics.
- Regression head predicts the target feature vector.
- Inverse scaler returns predictions to target scale.
- Evaluator reports MSE, MAE, and R2.

**Output:** A reconstructed depth/video feature vector.

**Metric:** R2 (higher is better). Minimal `-0.0153`, neural MLP `-0.0102`.

**Junior mental model:** This is feature-level imagination: can the model infer what another sensor would see?

**Current limitation:** This reconstructs compressed features, not raw pixels, depth maps, meshes, NeRFs, or Gaussian splats.

### Temporal Order Verification (`temporal_order`)

**Research name:** Temporal Order Verification

**Family:** diagnostic; pairwise classifier; D. Scene Reconstruction & World Modeling.

**Goal:** Tell whether two nearby windows are in the correct time order.

**Case study:** If window A shows reaching and window B shows pouring, the model should distinguish A then B from B then A.

**Input:** A pair of adjacent window vectors, plus their difference vector.

**Middle process modules:**
- Pair builder creates correct-order and reversed-order examples.
- Feature combiner concatenates first window, second window, and their difference.
- Binary classifier predicts correct vs reversed.
- Evaluator reports F1, precision, and recall.
- Diagnostic reader interprets whether features encode local time direction.

**Output:** A binary label: correct order or reversed order.

**Metric:** F1 (higher is better). Minimal `0.5400`, neural MLP `0.8520`.

**Junior mental model:** This asks whether the representation knows which moment came first.

**Current limitation:** It only tests local ordering, not long-term planning or causality.

### Multimodal Synchronization Detection (`misalignment_detection`)

**Research name:** Cross-Modal Misalignment Detection

**Family:** diagnostic; pairwise classifier; B. 3D/4D Reconstruction & Neural Rendering.

**Goal:** Detect when modalities that should match are shifted out of sync.

**Case study:** Motion from a pouring moment is paired with video/depth from several windows later. The task asks the model to detect that mismatch.

**Input:** A motion-side feature group and a visual/depth-side feature group, either aligned or artificially shifted.

**Middle process modules:**
- Alignment builder creates positive pairs from the same time window.
- Shift builder creates negative pairs by offsetting one modality group.
- Feature combiner joins both sides into one example.
- Binary classifier predicts aligned vs misaligned.
- Evaluator reports F1 and accuracy.

**Output:** A binary label: aligned or shifted.

**Metric:** F1 (higher is better). Minimal `0.5052`, neural MLP `0.7153`.

**Junior mental model:** This is a synchronization alarm for multimodal data.

**Current limitation:** Synthetic shifts are useful diagnostics but do not solve calibration, reconstruction, or mapping by themselves.