README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: viewer/train.csv
dataset_info:
  features:
    - name: package_id
      dtype: string
    - name: task_name
      dtype: string
    - name: task_description
      dtype: string
    - name: modalities
      dtype: string
    - name: package_path
      dtype: string
    - name: training_ready_path
      dtype: string
    - name: package_size_gb
      dtype: float64
    - name: validation
      dtype: string
  splits:
    - name: train
      num_examples: 8
license: cc-by-nc-4.0
task_categories:
  - robotics
  - depth-estimation
  - image-segmentation
  - keypoint-detection
  - time-series-forecasting
tags:
  - egocentric
  - rgb-d
  - emg
  - imu
  - hand-object-contact
  - per-finger-force
  - robotics
pretty_name: Egocentric RGB-D + EMG/IMU Daily Activity Dataset

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.