airoa-org/airoa-moma

HSR Household Service Robot Teleoperation Dataset

Dataset Overview

This dataset contains 25,469 episodes of household service robot teleoperation data collected using Toyota's Human Support Robot (HSR) platform. The dataset focuses on primitive action (PA) household manipulation tasks performed through human teleoperation, providing high-quality demonstrations for robot learning research.

Key Statistics

• Total Episodes: 25,469
• Total Frames: 10,180,700 (94 hours)
• Task Success Rate: 97.4% (24,799 successful episodes)
• Average Episode Length: 399.73 frames (13.3 seconds)
• Dataset Version: 1.0
• Last Updated: September 2025
• Total Size: ~92 GB

Task Distribution

The dataset covers 7 primary household task categories:

Task Category	Episodes	Percentage	Description
Cloth Manipulation	7,872	30.9%	Opening towel stand, hanging/folding towels
Coffee Making	6,052	23.8%	Complete coffee preparation workflow
Dishwashing	5,017	19.7%	Loading/unloading dishwasher operations
Toast Baking	3,512	13.8%	Bread preparation and toaster operations
Desk Lamp Control	1,968	7.7%	Button press and chain pull light control
Slipper Organization	1,048	4.1%	Arranging slippers in rack
Other Tasks	~700	<3%	Miscellaneous household tasks

Complete Short Horizon Task List

The dataset contains 7 distinct short horizon tasks that combine multiple primitive actions:

Rank	Short Horizon Task	Episodes	Percentage	Task Category
1	Open the towel stand and hang the towel	7,872	30.9%	Cloth Manipulation
2	Make coffee	6,052	23.8%	Coffee Making
3	Washing dishes in the dishwasher	5,017	19.7%	Dishwashing
4	Bake a toast	3,512	13.8%	Toast Baking
5	Press the button to turn the desk lamp on and off	1,271	5.0%	Desk Lamp Control
6	Stand the slippers in the slipper rack	1,048	4.1%	Slipper Organization
7	Pull the chain to turn the desk lamp on or off	697	2.7%	Desk Lamp Control

Top Individual Primitive Tasks

1. Hang the towel on the towel stand (1,362 episodes)
2. Grab the towel hanging on the towel stand (1,314 episodes)
3. Put the towel into the basket (1,306 episodes)
4. Open the towel stand (1,300 episodes)
5. Open the lid of the coffee maker (949 episodes)

Basic Skill Distribution

Analysis of primitive actions reveals the fundamental manipulation skills required:

Rank	Basic Skill	Occurrences	Percentage	Description
1	Open	38,905	15.7%	Opening containers, doors, stands
2	Place	38,852	15.7%	Placing objects in specific locations
3	Close	29,867	12.1%	Closing containers, doors, lids
4	Grab	29,101	11.7%	Grasping and holding objects
5	Pick	26,556	10.7%	Picking up objects from surfaces
6	Pull	22,527	9.1%	Pulling chains, handles, drawers
7	Push	20,501	8.3%	Pushing buttons, trays, objects
8	Put	18,263	7.4%	Putting objects into containers
9	Take	9,846	4.0%	Taking objects from locations
10	Press	8,859	3.6%	Pressing buttons and controls
11	Hang	8,580	3.5%	Hanging objects on stands/hooks
12	Remove	7,990	3.2%	Removing objects from containers
13	Fold	7,920	3.2%	Folding towel stands and objects
14	Insert	6,990	2.8%	Inserting objects into slots
15	Approach	6,105	2.5%	Moving toward target locations

Total Primitive Actions: ~247,892 across all episodes

The distribution shows a balanced representation of fundamental manipulation skills, with emphasis on:

• Container manipulation (Open/Close): 27.8% of all actions
• Object placement (Place/Put): 23.1% of all actions
• Object acquisition (Grab/Pick/Take): 26.4% of all actions
• Force application (Pull/Push/Press): 20.9% of all actions

Data Structure

Video Data

• Camera Setup: Dual RGB cameras (hand-mounted + head-mounted)
• Resolution: 640×480 pixels
• Framerate: 30 frames per second
• Format: MP4 files
• Camera Calibration: Included for both cameras with distortion parameters

Metadata (episodes.jsonl)

Each episode contains comprehensive metadata:

{
  "episode_index": 0,
  "tasks": ["Pull the chain to turn off the light."],
  "length": 729,
  "uuid": "a699601f-41e5-4678-865d-d9de37a010ad",
  "task_type": "PA",
  "task_success": true,
  "short_horizon_task": "Pull the chain to turn the desk lamp on or off",
  "primitive_action": ["Action sequence"],
  "label": "Operator001",
  "hsr_id": "robot003",
  "location_name": "location001",
  "calib": {...},
  "version": "1.0",
  "git_hash": "v4.0.0"
}

Key Metadata Fields

• episode_index: Unique episode identifier (0-25468)
• tasks: List of specific tasks performed in episode
• length: Number of timesteps in episode
• task_success: Boolean indicating task completion success
• short_horizon_task: High-level task description
• primitive_action: Detailed action sequence breakdown
• calib: Camera calibration parameters for head and hand cameras
• uuid: Uniquie identifier of high-level episode.
• label: Anonimized operator identifier.

Hardware Configuration

Robots

• Platform: Toyota Human Support Robot (HSR)
• Count: 8 robots (anonymized as robot001-robot008)
• Distribution:
  • robot002: 8,685 episodes (34.1%)
  • robot001: 4,286 episodes (16.8%)
  • robot005: 4,132 episodes (16.2%)
  • robot004: 3,068 episodes (12.0%)
  • Others: <10% each

Human Operators

• Count: 19 teleoperators (anonymized as Operator001-Operator019)
• Interface: HSR leader teleoperation system
• Primary Contributors:
  • Operator015: 12,408 episodes (48.7%)
  • Operator009: 3,071 episodes (12.1%)
  • Operator003: 2,175 episodes (8.5%)

Environment

• Location: Single laboratory environment (anonymized as location001)
• Setup: Controlled household environment with kitchen appliances and furniture

Data Anonymization

All personally identifiable information has been systematically anonymized using the mapping system defined in anonymization_mappings.json:

Anonymization Mappings

• Human Operators: 19 operators → Operator001-Operator019
• Robot IDs: 8 HSR units → robot001-robot008
• Locations: 1 lab environment → location001
• Git Hashes: Development commits → semantic versions (v1.0.0-v12.0.0)

Technical Specifications

Camera Calibration

Both cameras include complete calibration parameters:

• Intrinsic Matrix (K): 3×3 camera matrix
• Distortion Coefficients (D): Radial and tangential distortion
• Projection Matrix (P): 3×4 projection matrix
• Rectification Matrix (R): 3×3 rectification matrix

Usage Guidelines

Research Applications

• Robot Learning: Imitation learning from teleoperation demonstrations
• Computer Vision: Multi-view manipulation task understanding
• Task Planning: Hierarchical task decomposition analysis
• Human-Robot Interaction: Teleoperation interface studies

Quality Metrics

• Task Success Rate: 97.4% overall success rate
• Episode Length Distribution: 120-856 frames (avg: 399.73)
• Data Completeness: All episodes have corresponding hand and head camera videos
• Annotation Quality: Rich task decomposition with primitive action sequences

Limitations

• Environment Scope: Single laboratory setting may limit generalization
• Task Diversity: Focus on specific household tasks (7 main categories)
• Operator Variance: Uneven distribution across human operators
• Temporal Scope: Data collected during specific development phases

How to Download

Since the dataset have a lot of files, Hugging Face API can hit rate limit easily.

$ hf download airoa-org/airoa-moma --repo-type dataset

...

We had to rate limit you, you hit the quota of 1000 api requests per 5 minutes period. Upgrade to a PRO user or Team/Enterprise organization account (https://hf.co/pricing) to get higher limits. See https://huggingface.co/docs/hub/rate-limits

So we recommend you to download with Git over SSH.

First, follow the official instruction and set your public SSH key to Hugging Face.

cd ~/.ssh
ssh-keygen -t ed25519 -C "[email protected]" -f <key file>

Second, install Git LFS, which manages large files like videos. After installing Git LFS, you need to execute git lfs install once per user.

Then set up SSH Agent otherwise you will be asked SSH key pass phrase per files. One of the good documents is an istruction at GitHub Docs.

eval "$(ssh-agent -s)"
ssh-add ~/.ssh/<key file>

Additionally, add an entry at ~/.ssh/config

Host hf.co
  User git
  IdentityFile ~/.ssh/<key file>

Finally, you can clone;

git clone [email protected]:datasets/airoa-org/airoa-moma.git
cd airoa-moma
git lfs pull

Depending on your git configuration, git lfs pull can be run during git clone automatically. If you haven't pulled LFS managed files, they are just pointer text files.

$ file videos/chunk-000/observation.image.hand/episode_000000.mp4
videos/chunk-000/observation.image.hand/episode_000000.mp4: ASCII text

$ cat videos/chunk-000/observation.image.hand/episode_000000.mp4
version https://git-lfs.github.com/spec/v1
oid sha256:48277551133b1587c4c02cec6ee41f9a925565cf4d8aa9d0931f1d997d39c0a6
size 8488109

Once you pull LFS files, then they become regular files.

$ file videos/chunk-000/observation.image.hand/episode_000000.mp4
videos/chunk-000/observation.image.hand/episode_000000.mp4: ISO Media, MP4 Base Media v1 [ISO 14496-12:2003]

Citation

If you use this dataset in your research, please cite:

@article{airoa-moma-2025,
  author  = {Ryosuke Takanami, Petr Khrapchenkov, Shu Morikuni, Jumpei Arima, Yuta Takaba, Shunsuke Maeda, Takuya Okubo, Genki Sano, Satoshi Sekioka, Aoi Kadoya, Motonari Kambara, Naoya Nishiura, Haruto Suzuki, Takanori Yoshimoto, Koya Sakamoto, Shinnosuke Ono, Yo Ko, Daichi Yashima, Aoi Horo, Tomohiro Motoda, Kensuke Chiyoma, Hiroshi Ito, Koki Fukuda, Akihito Goto, Kazumi Morinaga, Yuya Ikeda, Riko Kawada, Masaki Yoshikawa, Norio Kosuge, Yuki Noguchi, Kei Ota, Tatsuya Matsushima, Yusuke Iwasawa, Yutaka Matsuo, Tetsuya Ogata},
  title   = {AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation},
  journal = {arXiv preprint},
  year    = {2025}
}