notes/episode_task_suite.md

Episode Task Suite

Script:

scripts/episode_task_suite.py

This script turns the single public Xperience-10M sample episode into many end-to-end tasks. It is designed for learning, debugging, and task design. It is not a generalization benchmark because the data is still one episode.

Run:

cd /path/to/Ropedia
source .venv/bin/activate
python scripts/episode_task_suite.py

Output:

outputs/episode_task_suite/

Shared setup:

sample episode: 5821 frames
windows:        1161
window size:    20 frames
stride:         5 frames
feature dim:    8378
split:          chronological, first 70% train and last 30% test

Implemented Tasks

Task	Input	Output	Main artifact
timeline_action	all modality window	current action label	timeline_action/metrics.json
timeline_subtask	all modality window	current subtask label	timeline_subtask/metrics.json
transition_detection	all modality window	steady vs action boundary	transition_detection/metrics.json
next_action	current all modality window	action 20 frames later	next_action/metrics.json
hand_trajectory_forecast	current all modality window	future 10-frame left/right hand joints	hand_trajectory_forecast/predictions.npz
contact_prediction	non-contact modalities	any body contact in window	contact_prediction/metrics.json
object_relevance	non-caption modalities	relevant object set	object_relevance/predictions.csv
caption_grounding	caption objects/interaction query + sensor candidates	matching time window	caption_grounding/metrics.json
cross_modal_retrieval	motion/IMU/camera query	matching depth/video window	cross_modal_retrieval/metrics.json
modality_reconstruction	motion/IMU/camera	depth/video feature vector	modality_reconstruction/predictions.npz
temporal_order	two adjacent windows	whether order is correct	temporal_order/metrics.json
misalignment_detection	motion+visual pair	aligned vs shifted	misalignment_detection/metrics.json

Minimal Model Architectures

All tasks share the same window builder unless a task explicitly removes a feature block to avoid label leakage.

raw sample episode
  -> 20-frame sliding windows, stride 5
  -> all-modality feature vector X_all, 8,378 dimensions
  -> chronological split, first 70% train and last 30% test
  -> train-only z-score scaler
  -> task-specific minimal head

The task suite intentionally uses simple heads:

Family	Formula	Tasks
Linear softmax	softmax(z(X)W + b), cross-entropy, L2	timeline_action, timeline_subtask, transition_detection, next_action, contact_prediction, temporal_order, misalignment_detection
Ridge regression/projection	dual ridge regression with L2=10 on z-scored X/Y	hand_trajectory_forecast, caption_grounding, cross_modal_retrieval, modality_reconstruction
Multi-label logistic	sigmoid(z(X)W + b), weighted object heads	object_relevance

Task-specific architecture details:

Task	Input tensor/vector	Minimal head	Output target
timeline_action	X_all, 8,378d	class-weighted linear softmax	current action label
timeline_subtask	X_all, 8,378d	class-weighted linear softmax	current subtask label
transition_detection	X_all, 8,378d	class-weighted linear softmax	steady vs transition near action boundary
next_action	X_all(t), 8,378d	class-weighted linear softmax	action at t+20 frames
hand_trajectory_forecast	X_all(t), 8,378d	ridge regression	future 10 frames of left/right hand joints, 1,260d
contact_prediction	all features except body_contacts and caption text, 7,335d	linear softmax on observed labels	any body contact in window
object_relevance	all features except caption text, 7,482d	multi-label logistic regression	34-object multi-hot vector
caption_grounding	sensor features, 7,482d, projected into 896d text space	ridge projection plus cosine ranking	matching time window for a text query
cross_modal_retrieval	motion/IMU/camera, 2,247d, projected into 5,096d visual space	ridge projection plus cosine ranking	matching depth/video window
modality_reconstruction	motion/IMU/camera, 2,247d	ridge regression	depth/video feature vector, 5,096d
temporal_order	[x_t, x_t+1, x_t+1-x_t], 25,134d	binary linear softmax	correct vs reversed order
misalignment_detection	motion plus visual pair, 7,343d	binary linear softmax	aligned vs shifted by 8 windows

Diagram:

docs/assets/task_architectures.png

Current Results

timeline_action:
  accuracy: 0.0292
  macro_f1: 0.0500
  note: future test region contains unseen action classes

timeline_subtask:
  accuracy: 0.0581
  macro_f1: 0.0495
  note: future test region contains unseen subtask classes

transition_detection:
  accuracy: 0.9253
  macro_f1: 0.6552
  boundary_f1: 0.2143

next_action:
  accuracy: 0.0345
  macro_f1: 0.0593
  note: same unseen-future-class problem as timeline_action

hand_trajectory_forecast:
  MPJPE: 0.8223
  final-frame MPJPE: 1.0650

contact_prediction:
  accuracy: 1.0000
  note: degenerate on this sample because the binary contact label has only one class

object_relevance:
  micro_f1: 0.1839
  macro_f1: 0.0643

caption_grounding:
  top1: 0.0029
  top5: 0.0115
  MRR: 0.0172

cross_modal_retrieval:
  top1: 0.1494
  top5: 0.3764
  top10: 0.4741
  MRR: 0.2634

modality_reconstruction:
  R2: -0.0160

temporal_order:
  accuracy: 0.4612
  f1: 0.5487

misalignment_detection:
  accuracy: 0.5029
  f1: 0.4866

How To Read These Results

Low scores are useful here. They show which tasks are not learnable from this one chronological sample with this minimal model.

The strongest signal is cross_modal_retrieval: motion/IMU/camera features can retrieve the matching depth/video window better than random. That means the modalities are synchronized and contain shared temporal structure.

The weakest supervised timeline tasks are weak mainly because of the split. The last 30% of a single ordered episode contains actions/subtasks not present in the first 70%, so a classifier trained on the first part cannot predict labels it never saw.

For serious research, keep the same task code but change the dataset unit:

many episodes -> train episodes -> test unseen episodes

For single-episode learning, these tasks are best used as:

• data pipeline tests
• modality ablations
• label-alignment checks
• self-supervised retrieval experiments
• debugging templates before scaling to many episodes