Datasets:

socialbank
/

sample

+---
+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:
+  - robotics
+  - object-detection
+task_ids:
+  - imitation-learning
+  - action-segmentation
+  - pose-estimation
+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