| --- |
| annotations_creators: |
| - expert-generated |
| language_creators: |
| - found |
| language: |
| - ja |
| license: cc-by-nc-4.0 |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 1K<n<10K |
| source_datasets: |
| - original |
| task_categories: |
| - other |
| task_ids: |
| - pose-estimation |
| - grasping |
| - task-planning |
| paperswithcode_id: null |
| pretty_name: FieldData Outdoor Labor Motion Dataset |
| tags: |
| - physical-ai |
| - embodied-ai |
| - motion-capture |
| - manipulation |
| - outdoor-robotics |
| - painting |
| - graffiti-removal |
| - construction |
| - human-robot-learning |
| --- |
| |
| # Dataset Card: FieldData Outdoor Labor Motion Dataset (FOLMD) |
|
|
| ## Table of Contents |
|
|
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks](#supported-tasks) |
| - [Data Collection](#data-collection) |
| - [Dataset Structure](#dataset-structure) |
| - [Annotations](#annotations) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [Dataset Creation](#dataset-creation) |
| - [Licensing Information](#licensing-information) |
| - [Citation](#citation) |
| - [Contact](#contact) |
|
|
| --- |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** https://fielddata.jp |
| - **Repository:** https://huggingface.co/datasets/fielddata-jp/folmd |
| - **Paper:** [Coming soon] |
| - **Point of Contact:** contact@fielddata.jp |
|
|
| ### Dataset Summary |
|
|
| **FieldData Outdoor Labor Motion Dataset (FOLMD)** is a multi-modal motion dataset of professional outdoor workers performing real-world manual labor tasks in urban environments in Japan. |
|
|
| This dataset is designed to support the development of **physical AI foundation models** and **embodied robotic systems** targeting outdoor maintenance and construction tasks. All data was collected from professional workers at actual job sites — not from staged laboratory environments. |
|
|
| The v1.0 release covers a complete two-phase urban fence restoration task: |
| 1. **Graffiti removal** using chemical solvent and rag wiping |
| 2. **Repainting** with black paint using brushes at multiple heights and postures |
|
|
| Data modalities include egocentric RGB video, depth maps (Intel RealSense D435i), full-body IMU (8-point, Mbientlab MetaMotionS), glove-based grasp force (Tekscan Grip System), and annotated 3D skeleton sequences (SMPL format). |
|
|
| --- |
|
|
| ## Why This Dataset? |
|
|
| | Challenge for Robots | What This Dataset Provides | |
| |---|---| |
| | Vertical surface contact control | Sustained brush pressure on metal slat fence (9–25 N) | |
| | Low-posture precision work | Deep crouch / kneeling brushwork at ground level | |
| | Boundary-aware manipulation | Painting within 10 mm of masking tape edges | |
| | Multi-posture task transitions | Standing → crouching → kneeling within a single session | |
| | Visual quality assessment | Workers pausing to inspect coverage, detecting missed spots | |
| | Multi-agent coordination | 3–4 workers dividing fence height and working in parallel | |
|
|
| These tasks involve sustained contact with irregular vertical surfaces, fine motor control under physical load, and situated decision-making — all critical challenges for next-generation manipulation models. |
|
|
| --- |
|
|
| ## Supported Tasks |
|
|
| - **Imitation Learning (IL):** Action-annotated demonstrations suitable for behavior cloning and inverse reinforcement learning |
| - **Vision-Language-Action (VLA):** Paired egocentric video and action labels for multimodal training |
| - **Pose Estimation:** Full-body SMPL skeleton data across 6 posture categories |
| - **Action Segmentation:** 7-class taxonomy with fine-grained sub-actions and temporal boundaries |
| - **Force Control Learning:** Grasp pressure profiles for 5 grip types across different tools and surfaces |
| - **Contact-Rich Manipulation:** Depth + force data for learning compliant surface following |
|
|
| --- |
|
|
| ## Data Collection |
|
|
| ### Sensor Setup |
|
|
| | Modality | Device | Specs | Sample Rate | |
| |---|---|---|---| |
| | Egocentric RGB | GoPro Hero 12 | 1920×1080 | 60 fps | |
| | Depth + RGB | Intel RealSense D435i | 848×480 | 30 fps | |
| | Full-body IMU (×8) | Mbientlab MetaMotionS | Accel + Gyro + Euler | 200 Hz | |
| | Grasp Force Glove | Tekscan Grip System | 24 cells, right hand | 100 Hz | |
| | 3D Skeleton | Computed from IMU | SMPL format | 30 fps | |
| | External cameras | GoPro Hero 12 (×3) | 1920×1080 | 60 fps | |
|
|
| **Temporal synchronization:** All sensors synchronized via GPS-PPS hardware sync signal. Timestamp accuracy: ±0.8 ms. |
|
|
| ### Sensor Placement |
|
|
| ``` |
| Head: GoPro (front) + RealSense (side) |
| R Wrist: IMU |
| L Wrist: IMU |
| R Elbow: IMU |
| L Elbow: IMU |
| Waist: IMU |
| R Knee: IMU |
| L Knee: IMU |
| R Hand: Force glove (Tekscan) |
| ``` |
|
|
| ### Collection Protocol |
|
|
| - Workers are professional outdoor maintenance staff with 2–10 years of experience |
| - Written informed consent obtained from all participants |
| - Data collected at real urban job sites (Tokyo metropolitan area, Japan) |
| - Session begins with 5-minute sensor calibration and zeroing |
| - Workers perform tasks naturally without scripted movements |
| - All sessions include workers of mixed skill levels (novice / intermediate / experienced) |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| ``` |
| folmd-v1.0/ |
| ├── sessions/ |
| │ └── session_20210327_001/ |
| │ ├── raw/ |
| │ │ ├── gopro_head.mp4 # Egocentric RGB (60 fps) |
| │ │ ├── realsense_rgb.mp4 # RealSense RGB (30 fps) |
| │ │ ├── realsense_depth.bag # Depth stream (ROS bag) |
| │ │ ├── imu_r_wrist.csv # Right wrist IMU |
| │ │ ├── imu_l_wrist.csv # Left wrist IMU |
| │ │ ├── imu_r_elbow.csv # Right elbow IMU |
| │ │ ├── imu_l_elbow.csv # Left elbow IMU |
| │ │ ├── imu_waist.csv # Waist IMU |
| │ │ ├── imu_r_knee.csv # Right knee IMU |
| │ │ ├── imu_l_knee.csv # Left knee IMU |
| │ │ ├── imu_head.csv # Head IMU |
| │ │ └── force_glove_right.csv # Right hand force (24 cells) |
| │ ├── processed/ |
| │ │ ├── skeleton_smpl/ |
| │ │ │ └── frame_*.json # Per-keyframe SMPL skeleton |
| │ │ ├── depth_pointcloud/ |
| │ │ │ └── frame_*.pcd # Per-keyframe 3D point cloud |
| │ │ └── action_segments.json # Full annotation (Tier 2) |
| │ └── metadata/ |
| │ ├── session_info.json |
| │ ├── sensor_calibration.json |
| │ └── quality_report.pdf |
| ├── dataset_card.md # This file |
| ├── annotation_guidelines.pdf # Labeling rulebook (EN) |
| └── sample/ |
| └── 30min_annotated_sample/ # Free sample — see Licensing |
| ``` |
|
|
| --- |
|
|
| ## Annotations |
|
|
| ### Action Taxonomy (7 Primary Labels) |
|
|
| | Action Label | Description | Avg Duration (sec) | |
| |---|---|---| |
| | `solvent_application` | Applying chemical solvent to dissolve graffiti using rag | 45–180 | |
| | `scrub_with_rag` | Physical scrubbing to remove dissolved paint | 10–60 | |
| | `paint_application` | Applying paint with brush/roller at mid-to-upper height | 30–300 | |
| | `detail_paint_application` | Precision brushwork at edges, slat gaps, base boundaries | 10–120 | |
| | `visual_inspection` | Standing back to scan surface for quality assessment | 5–30 | |
| | `material_preparation` | Handling tools, mixing paint, preparing supplies | 5–60 | |
| | `masking` | Applying or adjusting masking tape | 30–180 | |
|
|
| ### Posture Labels (6 Categories) |
|
|
| ``` |
| standing_upright — full height, minimal trunk lean |
| standing_slight_lean — 10–20° trunk forward flexion |
| crouching_moderate — knee flexion 60–90° |
| crouching_deep — knee flexion > 90° |
| kneeling_single_knee — one knee on ground |
| kneeling_double_knee — both knees on ground |
| ``` |
|
|
| ### Force Labels (Qualitative) |
|
|
| ``` |
| minimal < 5 N (passive hold / inspection) |
| low 5–10 N (precision brush / fine detail) |
| medium 10–20 N (normal painting stroke) |
| high 20–30 N (scrubbing / removal force) |
| very_high > 30 N (heavy scrub on resistant surface) |
| ``` |
|
|
| ### Annotation Layers |
|
|
| | Layer | Content | Completeness | |
| |---|---|---| |
| | Layer 1 | Action segmentation (start/end time + label) | 100% | |
| | Layer 2 | Object + surface + environment labels | 100% | |
| | Layer 3 | Skill quality label (expert/intermediate/novice) | 100% | |
| | Layer 4 | Failure cases and recovery actions | 100% | |
|
|
| **Inter-annotator agreement (Cohen's κ):** 0.94 |
|
|
| --- |
|
|
| ## Data Fields |
|
|
| ### `action_segments.json` |
| |
| | Field | Type | Description | |
| |---|---|---| |
| | `session_id` | string | Unique session identifier | |
| | `frame_id` | string | Keyframe identifier | |
| | `timestamp_sec` | float | Time from session start (seconds) | |
| | `phase` | string | `graffiti_removal` or `repaint_application` | |
| | `action_label` | string | Primary action (see taxonomy above) | |
| | `sub_action` | string | Fine-grained sub-action description | |
| | `worker_id` | string | Worker identifier (W01–W04) | |
| | `body_posture` | string | Posture category | |
| | `working_height` | string | Vertical zone on fence being worked | |
|
|
| ### `imu_*.csv` |
| |
| | Field | Type | Unit | Description | |
| |---|---|---|---| |
| | `timestamp_sec` | float | s | Synchronized timestamp | |
| | `accel_x/y/z_g` | float | g | Linear acceleration | |
| | `gyro_x/y/z_dps` | float | °/s | Angular velocity | |
| | `euler_roll/pitch/yaw_deg` | float | ° | Fused orientation estimate | |
|
|
| ### `force_glove_right.csv` |
|
|
| | Field | Type | Unit | Description | |
| |---|---|---|---| |
| | `timestamp_sec` | float | s | Synchronized timestamp | |
| | `cell_{name}_N` | float | N | Force at each of 24 sensor cells | |
| | `total_force_N` | float | N | Sum across all active cells | |
| | `grip_type` | string | — | Inferred grip classification | |
|
|
| ### `realsense_depth.bag` (ROS bag) |
| |
| | Topic | Format | Description | |
| |---|---|---| |
| | `/camera/depth/image_rect_raw` | sensor_msgs/Image | 16-bit depth (mm) | |
| | `/camera/color/image_raw` | sensor_msgs/Image | RGB aligned to depth | |
| | `/camera/depth/color/points` | sensor_msgs/PointCloud2 | Fused RGBD point cloud | |
|
|
| --- |
|
|
| ## Data Splits |
|
|
| | Split | Sessions | Hours | Notes | |
| |---|---|---|---| |
| | Sample (free) | 1 | 0.5 | Available without license agreement | |
| | Full v1.0 | 10 | 50 | Commercial license required | |
| | Planned v2.0 | 50+ | 300+ | Additional tasks: garbage collection, weeding, building cleaning | |
|
|
| --- |
|
|
| ## Dataset Creation |
|
|
| ### Motivation |
|
|
| Physical AI and embodied robot models are rapidly advancing, but publicly available training data for **outdoor real-world manipulation tasks** remains extremely scarce. Existing motion datasets focus primarily on laboratory manipulation, household tasks, or driving — leaving a large gap in construction, maintenance, and urban service work. |
|
|
| This dataset addresses that gap by collecting data from professional Japanese workers performing skilled outdoor maintenance tasks. Japan's workforce is recognized globally for precision, safety discipline, and consistent execution — making it an ideal source of high-quality demonstration data. |
|
|
| ### Collection Methodology |
|
|
| - Task protocols defined in cooperation with professional site supervisors |
| - Sessions conducted at real operational job sites |
| - Multiple skill levels included to capture expert vs. novice motion differences |
| - Both "successful" and "recovery from failure" motion captured and labeled |
| - All data cleaned, synchronized, and formatted to be pipeline-ready |
|
|
| ### Known Limitations |
|
|
| - All data collected in Tokyo metropolitan area — urban environment bias |
| - Nighttime and rainy-weather sessions represent < 15% of current dataset |
| - Force glove covers right hand only |
| - Skeleton estimation accuracy degrades at extreme joint angles (> 110°) |
|
|
| --- |
|
|
| ## Licensing Information |
|
|
| This dataset is released under **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**. |
|
|
| - ✅ Free to use for academic and non-commercial research |
| - ✅ Free 30-minute sample available without registration |
| - ✅ Redistribution permitted with attribution |
| - ❌ Commercial use requires a separate commercial license |
|
|
| For commercial licensing inquiries, please contact: **contact@fielddata.jp** |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this dataset in your research, please cite: |
|
|
| ```bibtex |
| @dataset{fielddata2025folmd, |
| author = {FieldData Japan}, |
| title = {FieldData Outdoor Labor Motion Dataset (FOLMD) v1.0}, |
| year = {2025}, |
| publisher = {HuggingFace}, |
| url = {https://huggingface.co/datasets/fielddata-jp/folmd}, |
| note = {Multi-modal motion dataset for physical AI training — outdoor maintenance tasks, Tokyo, Japan} |
| } |
| ``` |
|
|
| --- |
|
|
| ## Contact |
|
|
| **FieldData Japan** |
| - Email: contact@fielddata.jp |
| - HuggingFace: [fielddata-jp](https://huggingface.co/fielddata-jp) |
| - X (Twitter): [@fielddata_jp](https://twitter.com/fielddata_jp) |
|
|
| We welcome collaboration inquiries from robotics researchers and companies interested in: |
| - Custom data collection for specific task domains |
| - Expanding the dataset to new outdoor task categories |
| - Joint research and co-authorship opportunities |
|
|
