--- license: cc-by-nc-4.0 tags: - egocentric - robotics - embodied-ai - ego-task - robox configs: - config_name: clips data_files: - split: train path: data/train.jsonl - split: validation path: data/val.jsonl - split: test path: data/test.jsonl --- # RoboX-EgoTask A premium egocentric dataset of real-world hand and object task demonstrations, recorded in first person through RoboX. This release bundles 17 task clips drawn from 5 full recordings (about 6 minutes of footage), each pairing clean RGB video with synchronized, robotics ready annotations: MediaPipe hand keypoints (21 joints, 2D and 3D), 6DoF camera pose, IMU, depth metadata, body pose, person segmentation, and per frame trajectory signals. It is built for embodied AI and egocentric robotics research, with predefined train, validation and test splits and a motion quality score on every clip. ## Examples All 17 clips are shown below with the QA overlay so the joint tracking is visible: MediaPipe hand landmarks rendered on top (left hand blue, right hand orange). The clips and recordings that ship for training are clean RGB without these overlays. Clips are grouped by the recording they came from. ### Making tea (kitchen task)

Kitchen task demonstration
Opening a box of tea

Selecting a tea bag from the box
Placing the tea bag into the mug

Pouring hot water from the kettle into the mug
### Loading the dishwasher (object transfer)

Opening the dishwasher door
Placing a blue plate with cutlery inside

Placing a white plate with cutlery inside
Placing a pot with its lid inside

Closing the dishwasher door
### Picking up a GoPro box and accessory (object transfer)

Picking up the GoPro box from the shelf
Examining the GoPro box on the desk

Opening the bag with the head strap accessory
Placing the head strap accessory on the desk
### Cleaning the sink

Spraying cleaning product onto the sink
Scrubbing the sink with a sponge
### Wiping a computer case

Wiping the front panel with a cloth
## Dataset Summary | Property | Value | |----------|-------| | Recordings | 5 | | Clips | 17 | | Duration | 5 min 59 sec | | Contributors | 3 (anonymized) | | Campaign | EgoTask | | Export date | 2026-05-28 | ## Collection Methodology Recordings are captured via RoboX as first-person egocentric videos with synchronized metadata. Depending on campaign type and device capability, recordings include: - hand keypoints (21 joints, 2D + 3D) - device IMU (accelerometer, gyroscope, gravity) - 6DoF camera pose (position, orientation, velocity) - camera intrinsics and exposure per frame - scene structure and light estimates ## Annotation Pipeline 1. On-device ARKit sensors provide hand keypoints, 6DoF camera pose, IMU, and camera intrinsics per frame 2. Object detection produces per-clip bounding boxes with class labels 3. Scene, action, and narration annotations are aligned to clip boundaries and clip-relative timestamps ## Dataset Tiers Two tier axes live on every clip: 1. **`dataset_tier`**: the **bundle-level buyer tier** the export was packaged at (`base` / `pro` / `premium` / `custom`). Same value on every clip in this bundle (see `manifest.json::dataset_tier`). Drives which annotation streams the runner emits. 2. **`buyer_tier_eligible`**: the **highest tier the clip's source data supports**, computed from `source_modalities_available`. Independent of `dataset_tier`, a Premium bundle can contain clips that only justify Pro or Base, and a Base bundle still surfaces eligibility so consumers can spot data that *could* be re-bundled at a higher tier. Buyer tiers map to source modalities as follows: - **`base`**: video asset plus any AI-derived labels (object detections, scene / activity tags). No structured robotics sensor data. - **`pro`**: `base` plus the motion stack: 6DoF camera pose, camera intrinsics, IMU, hand keypoints when captured, and depth (LiDAR / TrueDepth) when available. - **`premium`**: `pro` plus body pose (19-joint skeleton with stable `person_id`) and / or person segmentation (instance bbox per frame with the same `person_id` pool). Tier distribution for this bundle (counts the clips' `buyer_tier_eligible`): - `base`: 0 clips (0.0%) - `pro`: 0 clips (0.0%) - `premium`: 17 clips (100.0%) A finer-grained **`sensor_tier`** label (`rgbd_navigation` / `rgbd_full` / `full` / `motion_only` / `vision_only` / `video_only`) is also stamped on every clip with a sensor bundle. It exposes the capture topology beyond the three-tier buyer ladder so robotics buyers can pick, e.g. "depth-equipped EgoNav only" without enumerating modality flags. Use `sensor_tier` (when present), `exported_modalities` and `buyer_tier_eligible` in `clips.jsonl` to filter by capability. `manifest.json::included_modalities` lists every modality actually contained in this export, and `excluded_modalities` lists modalities captured by the device but stripped by the active dataset tier. ## Bimanual Activity Every clip carries lightweight bimanual aggregates derived from per-frame hand state (`annotations/hand_state/`): - `is_bimanual_active`: `true` when both hands were jointly active for ≥ 10% of clip frames - `bimanual_active_percent`: fraction of frames where both hands were active (0–100) - `bimanual_active_duration_sec`: estimated seconds where both hands were active - `hand_state_distribution`: per-state frame counts: `no_hand_visible` / `left_only` / `right_only` / `both_visible` / `both_active` - `labels.bimanual`: any of `bimanual_grasp` (both hands closed-grip with object contact for ≥ 20% of frames), `handoff` (`in_hand` side flips within 1.5 s), `coordinated_action` (sustained two-handed activity, ≥ 40% of frames, that did not qualify as `bimanual_grasp`) A "hand is active" when it is visible AND either (a) holds a closed grip (`power_grip` / `pinch` / `precision_grip`), (b) overlaps any tracked object's bbox, or (c) reports a small thumb–index pinch distance. ## Dataset Profiles When this bundle was exported with a named **dataset profile**, the resolved recipe is recorded in `manifest.json::profile`. Profiles pin the bundle to a single campaign and apply extra clip-level filters on top of the chosen tier, for example, `egograsp_bimanual_v1` keeps only EgoGrasp Premium clips with `bimanual_active_percent ≥ 50`. ## Motion Quality Scoring Every clip carries a `motion_quality_score` (0–100) derived from device telemetry the runner already extracts. Buyers can sort/filter low-quality clips without opening any MP4: - `clips.jsonl::motion_quality_score`: composite score, 0–100 - `clips.jsonl::quality_tier`: one of `excellent` (≥ 85), `good` (≥ 65), `usable` (≥ 40), `reject` (< 40) - `clips.jsonl::motion_quality.*`: component metrics that fed the score: - `motion_smoothness`: inverse of mean angular-velocity jerk (handheld shakiness) - `activity_density_percent`: fraction of frames with any signal (hands / objects / non-trivial camera motion) - `active_frame_percent`: fraction of frames where a hand is actively engaged - `tracking_quality_percent`: fraction with ARKit `tracking_state == "normal"` - `low_light_percent`: fraction with ambient lumens below 100 lm - `motion_blur_risk_percent`: fraction with linear OR angular velocity above the motion-blur thresholds - `frame_jitter`: `{mean_interval_ms, std_interval_ms, max_deviation_ms, jitter_score}` from frame timing `manifest.json::quality` carries the dataset-wide tier distribution and mean score so consumers see overall bundle quality before drilling into clips. Missing telemetry (e.g. no light estimate on a legacy device) defaults to a neutral 50 contribution to the composite so older bundles are not penalised relative to fully-equipped captures. ## Universal Trajectory Segmentation In addition to the **campaign-specific** action segments under `annotations/actions/`, every clip carries a **campaign-agnostic** trajectory state stream derived from pose / IMU / Core Motion / hand activity signals. Robotics buyers training cross-campaign models can filter / join by trajectory state directly without ever consulting the campaign-specific taxonomy. Three states (priority order: `interaction` > `locomotion` > `idle`): - **`interaction`**: a hand is actively engaged with an object (`hands_active` or `interaction_active`). Walking-while-holding counts as `interaction`, not `locomotion`. - **`locomotion`**: the body / camera is translating through space. Triggered by linear velocity ≥ 0.2 m/s, sustained IMU user-acceleration ≥ 0.15 m/s², or Core Motion `walking`/`running`/`cycling`/`automotive` activity at non-low confidence. - **`idle`**: neither of the above. Hand visible but inactive, body stationary. Per-clip surface on `clips.jsonl::trajectory`: - `state_distribution` / `state_percent` / `state_duration_sec`: per-state frame counts, fraction, and seconds - `dominant_state`: most-common state (ties broken by priority order) - `segments[]`: merged timeline spans `{state, start_sec, end_sec, start_frame, end_frame, frame_count}` (segments shorter than 0.3 s are absorbed into their predecessor to suppress tracker hiccups) - `segment_count` / `frame_count` A flat `clips.jsonl::dominant_trajectory_state` field is duplicated alongside `dominant_state` so consumers can `WHERE dominant_trajectory_state = 'locomotion'` without unpacking the nested block. Per-frame stream at `annotations/trajectory/.jsonl` preserves the underlying signals (linear velocity, IMU magnitude, GPS activity) so consumers can derive their own state machines (e.g. a 4-state `walking_with_object` refinement) without re-running the pipeline. `manifest.json::trajectory` rolls up the bundle-wide dominant-state distribution and total seconds per state. ## Intended Use This dataset is intended for embodied AI and egocentric robotics research. ## Duration Distribution - `min_sec`: 2.5 - `max_sec`: 63.734 - `mean_sec`: 15.445 - `median_sec`: 12.0 ## Annotation Coverage - `hand_keypoints`: 17/17 clips (100.0%) - `hand_state`: 17/17 clips (100.0%) - `hand_landmarks_mediapipe`: 17/17 clips (100.0%) - `trajectory`: 17/17 clips (100.0%) - `object_tracks`: 16/17 clips (94.1%) - `object_tracks_temporal`: 0/17 clips (0.0%) - `object_tracks_ai_fallback`: 16/17 clips (94.1%) - `sensors`: 17/17 clips (100.0%) - `actions`: 17/17 clips (100.0%) - `environment`: 17/17 clips (100.0%) - `people`: 16/17 clips (94.1%) - `body_pose`: 2/17 clips (11.8%) - `segmentation`: 16/17 clips (94.1%) ## Annotation Coverage By Sensor Tier - `rgbd_full` (17 clips): actions 17/17 (100.0%), body_pose 2/17 (11.8%), environment 17/17 (100.0%), hand_keypoints 17/17 (100.0%), hand_state 17/17 (100.0%), object_tracks 16/17 (94.1%), people 16/17 (94.1%), segmentation 16/17 (94.1%), sensors 17/17 (100.0%), trajectory 17/17 (100.0%) ## Dataset Splits - `test`: 1 clips, 1 recordings, 1 contributors - `train`: 9 clips, 2 recordings, 1 contributors - `val`: 7 clips, 2 recordings, 1 contributors ## Object Distribution - No object-level distribution available ## Domain Distribution - No domain-level distribution available ## Structure - `recordings/`: Clean RGB full recordings (training-safe; no baked hand overlays). - `clips/`: Clean RGB task/action clips derived from recordings. - `overlays/`: Optional QA/debug preview videos with rendered hand landmarks (see `manifest.json::video_assets` and per-row `video` blocks in `metadata/recordings.jsonl` / `metadata/clips.jsonl`). Left hand `#4A90E2`, right hand `#FF8C42` when MediaPipe overlays are included. - `metadata/recordings.jsonl`: Recording-level index - `metadata/clips.jsonl`: Clip-level index (dataset index) - `metadata/contributors.jsonl`: Anonymized contributor info - `metadata/splits.jsonl`: Contributor-aware train/val/test split assignment - `taxonomy.json`: Object/domain mapping derived from clip labels - `VALIDATION_REPORT.json`: Bundle integrity and consistency checks - `SANITIZATION_REPORT.json`: Buyer-facing privacy and sensitive-content scan summary - `USAGE_TERMS.md`: Human-readable summary of license, commercial-use, exclusivity and buyer-assignment metadata (see also `manifest.json::release`) - `showcase/best_clips.json`: Auto-selected best representative clips per campaign - `notebooks/explore_dataset.ipynb`: Interactive exploration notebook (run in Jupyter) - `annotations/actions/`: Temporal action segments per clip - `annotations/hand_keypoints/`: Per-frame hand pose (21 joints). **As of schema v1.5** this stream is sourced from MediaPipe HandLandmarker running in the export backend, see `manifest.json::mediapipe_hands` and `clips.jsonl::hand_detection_source` for provenance. The on-device iOS Vision hands are still captured by the app but are no longer the canonical source in the export bundle; this guarantees left/right chirality is consistent across all annotation streams. - `annotations/hand_landmarks_mediapipe/`: Per-**video**-frame raw MediaPipe stream (one entry per frame at video fps, including frames where no hand was detected). Finer rate than `hand_keypoints` (which samples at iOS metadata rate, ~10 Hz). Same field shape as `hand_keypoints` entries. - `annotations/hand_state/`: Per-frame bimanual state (left/right visible + active flags, per-hand grip, per-hand `in_hand`). Always rides along with `hand_keypoints`. - `annotations/trajectory/`: Per-frame **universal** robotics trajectory state (`idle` / `locomotion` / `interaction`) plus the raw signals that produced it (linear velocity, IMU acceleration, Core Motion activity). Campaign-agnostic and orthogonal to the campaign-specific actions stored in `annotations/actions/`. - `annotations/object_tracks/`: Per-frame object bounding boxes - `annotations/sensors/`: IMU, 6DoF camera pose, intrinsics, plus a per-frame `depth` block (`available`, `resolution`, `stats`, `intrinsics`), full LiDAR/TrueDepth metadata when the device captured it (Pro+ tier) - `annotations/body_pose/`: Per-frame body skeleton (19 joints) with stable `person_id` (Premium tier only) - `annotations/segmentation/`: Per-frame person-segmentation bboxes with stable `person_id` and frame-level coverage ratio (Premium tier only) - `annotations/environment/`: Scene and environment labels per clip - `annotations/people/`: Clip-level people visibility and count derived from AI enrichment ## Sensor Data Description - `annotations/sensors/` stores aligned per-frame measurements for IMU, camera pose, intrinsics, exposure, light estimate and a `depth` block (`available`, `resolution`, `stats`, `intrinsics`, LiDAR/TrueDepth metadata; the dense pixel map is not persisted). Recording- and clip-level rollups (`depth_stats`, `depth_summary`, `lidar`) are surfaced in `metadata/recordings.jsonl` and `metadata/clips.jsonl` - `annotations/hand_keypoints/` stores per-frame hand observations including grip type and 2D/3D joint data when available - `annotations/body_pose/` stores per-frame body skeletons with a stable `person_id` (IoU-tracked across frames), `bbox_normalized`, per-joint 2D coordinates and confidence, and an optional `body_pose_summary` rollup - `annotations/segmentation/` stores per-frame person-segmentation results, one `persons[]` entry per detected human (bbox + stable `person_id`, IoU-tracked across frames; the same identity is shared with `annotations/body_pose/` when both signals fire on the same human), plus the device-reported `coverage_ratio`, `covered_pixel_count` and frame dimensions - `annotations/actions/` stores temporal clip-level action segments ## Limitations - Clean RGB videos in `recordings/` and `clips/` are the primary training source. Overlay videos under `overlays/`, when present, are rendered visualization assets for QA, debugging, and demo review only, do not treat them as raw sensor video. - object tracks are only present when source frames include object detections - this export is optimized for structured ML ingestion rather than human-readable storytelling ## Data Loading ### Clip metadata with the datasets library The clip-level metadata is exposed as a loadable config with `train`, `validation` and `test` splits. This dataset is gated, so authenticate first with `hf auth login` (or pass `token=True`). ```python from datasets import load_dataset ds = load_dataset("RoboXTechnologies/RoboX-EgoTask", "clips") print(ds) row = ds["train"][0] print(row["clip_id"], row["task_category"], row["narration"]) print(row["video_url"]) # clean RGB clip print(row["overlay_url"]) # same clip with hand landmarks rendered ``` ### Full per-frame annotations from a local copy The config above is a flat summary, one row per clip. The complete per-frame streams (hand keypoints, sensors, trajectory and more) ship as JSONL under `annotations/` and `metadata/`. Download or clone the repo, then read them directly: ```python import json from pathlib import Path root = Path("RoboX-EgoTask") # path to the downloaded repo clips = [json.loads(line) for line in (root / "metadata" / "clips.jsonl").read_text().splitlines()] hand_clips = [c for c in clips if (c.get("exported_modalities") or {}).get("hand_keypoints_2d")] print(clips[0]["clip_id"], clips[0]["labels"]) ``` ### Streaming clip videos into PyTorch A minimal `Dataset` that pulls each clip's MP4 on demand (cached after the first fetch) and decodes it to a tensor. Clips vary in length, so this uses `batch_size=1`; add a `collate_fn` that pads or samples a fixed number of frames to batch them. ```python import torch from torch.utils.data import Dataset, DataLoader from datasets import load_dataset from huggingface_hub import hf_hub_download import torchvision REPO = "RoboXTechnologies/RoboX-EgoTask" class EgoTaskClips(Dataset): def __init__(self, split="train"): self.rows = load_dataset(REPO, "clips", split=split) def __len__(self): return len(self.rows) def __getitem__(self, idx): row = self.rows[idx] path = hf_hub_download(REPO, f"clips/{row['clip_id']}.mp4", repo_type="dataset") video, _, _ = torchvision.io.read_video(path, output_format="TCHW") # (T, C, H, W) return {"clip_id": row["clip_id"], "video": video, "label": row["task_category"]} loader = DataLoader(EgoTaskClips("train"), batch_size=1, shuffle=True) batch = next(iter(loader)) print(batch["clip_id"], batch["video"].shape) ``` ## Citation ```bibtex @dataset{robox_egotask_2026, title={RoboX-EgoTask: An Egocentric Hand-Object Task Dataset}, author={RoboX Team}, year={2026}, url={https://huggingface.co/datasets/RoboXTechnologies/RoboX-EgoTask}, license={CC-BY-NC-4.0} } ``` ## License CC-BY-NC-4.0 Commercial use: ⛔ Not allowed, research and non-commercial use only For commercial licensing inquiries: contact@robox.to ## Usage Terms & Exclusivity `manifest.json::release` is the canonical machine-readable contract attached to this bundle and `USAGE_TERMS.md` is the buyer-readable summary of the same data. Fields: - `license_id`: short license slug (e.g. `CC-BY-NC-4.0` / `CC-BY-4.0` / `ROBOX-COMMERCIAL-1.0`). - `license_url`: link to the full license text. The full text also ships in this bundle's `LICENSE` file. - `commercial_use_allowed`: boolean derived from the license. - `exclusive`: when `true`, RoboX has agreed not to license the same underlying data to other parties for the duration of the exclusivity window. - `exclusive_until`: ISO 8601 expiry of the exclusivity arrangement (`null` for non-exclusive or perpetual-exclusive bundles). - `buyer_id` / `buyer_alias`: opaque buyer identifier + buyer-facing display string when the bundle was prepared for a specific buyer; both `null` otherwise. - `warnings`: informational slugs (e.g. `exclusive_without_buyer`) attached when the input combination is suspicious. The bundle still ships; operations reconciles these against the underlying contract. Conservative defaults apply when the export request omits a field, so a buyer or tester never receives a permissive license / exclusivity flag by accident: `CC-BY-NC-4.0`, `commercial_use_allowed=false`, `exclusive=false`, `buyer_id=null`.