package_id stringclasses 8
values | task_name stringclasses 8
values | task_description stringclasses 8
values | modalities stringclasses 1
value | package_path stringclasses 8
values | training_ready_path stringclasses 8
values | package_size_gb float64 0.35 7.61 | validation stringclasses 1
value |
|---|---|---|---|---|---|---|---|
washing-machine-laundry | Washing machine laundry | First-person recording of loading and operating a washing machine for laundry. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/washing-machine-laundry | packages/washing-machine-laundry/training_ready | 1.421 | pass |
tidy-bedroom | Tidy bedroom | First-person recording of tidying and organizing a bedroom. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/tidy-bedroom | packages/tidy-bedroom/training_ready | 5.739 | pass |
sweep-and-mop-floor | Sweep and mop floor | First-person recording of sweeping and mopping the floor. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/sweep-and-mop-floor | packages/sweep-and-mop-floor/training_ready | 6.804 | pass |
tidy-living-room | Tidy living room | First-person recording of tidying and organizing a living room. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/tidy-living-room | packages/tidy-living-room/training_ready | 4.079 | pass |
tidy-dining-room | Tidy dining room | First-person recording of tidying and organizing a dining room. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/tidy-dining-room | packages/tidy-dining-room/training_ready | 7.613 | pass |
hand-cream-application | Hand cream application | First-person recording of applying hand cream and manipulating a small handheld container. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/hand-cream-application | packages/hand-cream-application/training_ready | 0.535 | pass |
sink-hand-washing | Sink hand washing | First-person recording of washing hands at a sink. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/sink-hand-washing | packages/sink-hand-washing/training_ready | 0.346 | pass |
paper-towel-hand-wiping | Paper towel hand wiping | First-person recording of wiping hands with a paper towel. | RGB-D, EMG, IMU, hand keypoints, object masks, contact, per-finger force | packages/paper-towel-hand-wiping | packages/paper-towel-hand-wiping/training_ready | 0.377 | pass |
Egocentric RGB-D + EMG/IMU Daily Activity Dataset
This dataset contains first-person daily activity recordings with synchronized RGB-D video, wrist EMG/IMU signals, hand keypoints, object masks, hand-object contact annotations, per-finger force annotations, and semantic action segments.
Multimodal showcase video: RGB-D, hand joints, 3D hand projection, and object masks
Overview
The dataset is designed for egocentric embodied AI and robot learning in everyday household activities. RGB-D frames provide visual and geometric context, EMG/IMU streams capture hand activity, and contact-force annotations describe where fingers interact with objects and how force is distributed across the hand.
These modalities support research on manipulation understanding, imitation learning, action segmentation, contact-aware perception, multimodal behavior modeling, and robot training from human demonstrations.
Dataset Highlights
- First-person RGB-D recordings from daily household tasks.
- Synchronized wrist EMG/IMU streams aligned to the RGB frame timeline.
- Hand keypoints, object masks, hand-object contact annotations, and per-finger force annotations.
- Per-frame 21-point hand keypoint JSONL exports are included under
analysis/hand_keypoints/. - Public object-mask preview videos are included for each package under
analysis/object_mask_public/. - Semantic action segments for task-level and subtask-level analysis.
training_ready/exports for model and robot learning workflows.
Packages
| Package | Files | Size GB | Validation |
|---|---|---|---|
washing-machine-laundry |
334 | 1.503 | pass |
tidy-bedroom |
1246 | 6.076 | pass |
sweep-and-mop-floor |
1395 | 7.163 | pass |
tidy-living-room |
935 | 4.132 | pass |
tidy-dining-room |
1616 | 7.711 | pass |
hand-cream-application |
263 | 0.551 | pass |
sink-hand-washing |
157 | 0.353 | pass |
paper-towel-hand-wiping |
165 | 0.383 | pass |
Structure
packages/<package_id>/
source_stage_a/ # original lossless RGB-D and sensor export
clean/ # aligned timelines and corrected sensors
gold/ # episode and standard model exports
analysis/contact_force_v2/
analysis/hand_keypoints/
analysis/object_mask_public/
analysis/semantic_subtasks/
events/
training_ready/ # consolidated model-ready files
Training
For model or robot training, start from packages/<package_id>/training_ready/. It contains frame indexes, corrected EMG/IMU streams, semantic segments, contact-force tables, and standard imitation-learning exports when available.
Loader Examples
The repository includes two lightweight Python examples under examples/.
python examples/quickstart_loader.py --dataset-root .
python examples/training_sample_loader.py --dataset-root . --package-id hand-cream-application
quickstart_loader.py reads viewer/train.csv, lists all packages, and checks the expected public directories. training_sample_loader.py shows how to locate RGB/depth timelines, corrected EMG/IMU streams, semantic action segments, contact-force tables, RLDS episodes, and robomimic exports for one package.
The quickstart script uses only the Python standard library. The training sample script can also preview Parquet tables when pandas and pyarrow are installed.
Citation
If you use this dataset, please cite the dataset repository and reference the package IDs used in your experiments.
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