Rewrite README: accurate description of dataset content, pipeline, and data format

README.md

@@ -5,39 +5,59 @@ task_categories:
 tags:
   - lerobot
   - hand-pose
-  - rgb-d
   - humanoid
   - manipulation
   - 6dof
   - mediapipe
   - egocentric
-  - motion-semantics
+  - imitation-learning
 size_categories:
   - 10K<n<100K
 language:
   - en
-pretty_name: Dynamic Intelligence - Egocentric Human Motion Annotation Dataset
+pretty_name: Dynamic Intelligence - Humanoid Robot Training Dataset
 ---
 
-# Dynamic Intelligence - Egocentric Human Motion Annotation Dataset
+# Dynamic Intelligence — Humanoid Robot Training Dataset
 
-RGB-D hand manipulation dataset captured with iPhone 13 TrueDepth sensor for humanoid robot training. Includes 6-DoF hand pose trajectories, synchronized video, and semantic motion annotations.
+A first-person (egocentric) video dataset of human hand manipulation, designed for training humanoid robot policies via imitation learning. Each episode captures a person performing an everyday household task — folding clothes, moving dishes, opening doors — filmed from a head-mounted iPhone using its built-in LiDAR and depth sensors.
+
+The dataset pairs each video with frame-level 3D hand tracking and camera pose data, giving learning algorithms both the visual input and the corresponding spatial trajectories they need to reproduce the demonstrated behavior on a robot.
 
 ---
 
-## Dataset Overview
+## How it works
+
+**Recording setup.** A person wears an iPhone 13 Pro on their head (using a head mount). The phone runs the [Record3D](https://record3d.app/) app, which simultaneously captures:
+- RGB video at 30 FPS
+- Depth maps via the LiDAR sensor
+- 6-DoF camera pose from ARKit (position + orientation of the phone in the room)
+
+**Processing pipeline.** After recording, each episode goes through an offline pipeline:
+1. **Hand detection** — [MediaPipe](https://ai.google.dev/edge/mediapipe/solutions/vision/hand_landmarker) detects 2D hand landmarks in every RGB frame
+2. **3D reconstruction** — The 2D landmarks are projected into 3D space using the corresponding depth map, producing real-world XYZ positions (in cm) relative to the camera
+3. **Action computation** — Frame-to-frame deltas are computed for both the camera and hand positions, representing the "actions" a robot would need to take
+
+**Result.** Each episode contains a synchronized video and a parquet file with per-frame 3D observations and actions, formatted for the [LeRobot](https://github.com/huggingface/lerobot) framework.
+
+---
 
-| Metric | Value |
-|--------|-------|
-| Episodes | 145 |
-| Total Frames | ~59,000 |
-| FPS | 30 |
-| Tasks | 12 manipulation tasks |
+## Dataset overview
 
-### Task Distribution
+| | |
+|---|---|
+| **Episodes** | 145 |
+| **Total data frames** | ~59,000 |
+| **Video FPS** | 30 |
+| **Tasks** | 12 household manipulation tasks |
+| **Format** | [LeRobot v2.0](https://github.com/huggingface/lerobot) |
+| **Sensor** | iPhone 13 Pro (RGB + LiDAR + ARKit) |
+| **Perspective** | Egocentric (head-mounted) |
 
-| Task | Description | Episodes | Count |
-|------|-------------|----------|-------|
+### Tasks
+
+| # | Task instruction | Episodes | Count |
+|---|------------------|----------|-------|
 | 1 | Fold the t-shirt on the bed. | 0–7 | 8 |
 | 2 | Pick up the two items on the floor and put them on the bed. | 8–17 | 10 |
 | 3 | Fold the jeans on the bed. | 18–27 | 10 |
@@ -53,54 +73,65 @@ RGB-D hand manipulation dataset captured with iPhone 13 TrueDepth sensor for hum
 
 ---
 
-## Repository Structure
+## What's in the data
 
-```
-humanoid-robots-training-dataset/
-│
-├── data/
-│   ├── chunk-000/                    # Parquet files (episodes 0-99)
-│   └── chunk-001/                    # Parquet files (episodes 100-144)
-│
-├── videos/
-│   ├── chunk-000/rgb/                # MP4 videos (episodes 0-99)
-│   └── chunk-001/rgb/                # MP4 videos (episodes 100-144)
-│
-├── meta/
-│   ├── info.json                     # Dataset configuration (LeRobot format)
-│   ├── stats.json                    # Feature statistics
-│   ├── events.json                   # Disturbance & recovery annotations
-│   └── annotations_motion_v1_frames.json
-│
-└── README.md
-```
+Each episode has two files: a **video** (`.mp4`) and a **parquet** table with one row per tracked frame.
 
----
+### Observations (what the robot sees)
+
+| Column | Shape | Unit | Description |
+|--------|-------|------|-------------|
+| `observation.camera_pose` | float[6] | cm, degrees | Position (x, y, z) and orientation (roll, pitch, yaw) of the head-mounted camera in the room. Comes from ARKit's visual-inertial odometry. |
+| `observation.left_hand` | float[9] | cm | 3D positions of 3 keypoints on the left hand: wrist, thumb tip, and index fingertip (x, y, z each). |
+| `observation.right_hand` | float[9] | cm | 3D positions of 3 keypoints on the right hand: wrist, index fingertip, and middle fingertip (x, y, z each). |
+
+### Actions (what the robot should do)
 
-## Data Schema
+| Column | Shape | Description |
+|--------|-------|-------------|
+| `action.camera_delta` | float[6] | Frame-to-frame change in camera pose (dx, dy, dz, droll, dpitch, dyaw). Represents head movement. |
+| `action.left_hand_delta` | float[9] | Frame-to-frame change in left hand keypoint positions. |
+| `action.right_hand_delta` | float[9] | Frame-to-frame change in right hand keypoint positions. |
 
-### Parquet Columns (per frame)
+### Metadata columns
 
 | Column | Type | Description |
 |--------|------|-------------|
-| `episode_index` | int64 | Episode number (0–144) |
-| `frame_index` | int64 | Frame within episode |
-| `timestamp` | float64 | Time in seconds |
-| `language_instruction` | string | Task description |
-| `observation.camera_pose` | float[6] | Camera 6-DoF (x, y, z, roll, pitch, yaw) |
-| `observation.left_hand` | float[9] | Left hand keypoints |
-| `observation.right_hand` | float[9] | Right hand keypoints |
-| `action.camera_delta` | float[6] | Camera delta 6-DoF |
-| `action.left_hand_delta` | float[9] | Left hand delta keypoints |
-| `action.right_hand_delta` | float[9] | Right hand delta keypoints |
-
-### Coordinate System
-- Origin: Camera (iPhone TrueDepth)
-- X: Right, Y: Down, Z: Forward (into scene)
+| `episode_index` | int | Which episode (0–144) |
+| `frame_index` | int | Frame number within the episode |
+| `timestamp` | float | Time in seconds from episode start |
+| `language_instruction` | string | Natural language task description (same for all frames in an episode) |
+| `next.done` | bool | Whether this is the last frame of the episode |
+
+### Coordinate system
+
+All 3D positions are relative to the camera:
+- **X** → right
+- **Y** → down
+- **Z** → forward (into the scene)
+
+Hand values of `[0, 0, 0]` mean the hand was not detected in that frame (e.g. out of view or occluded).
+
+---
+
+## File structure
+
+```
+├── data/
+│   ├── chunk-000/          # Parquet files for episodes 0–99
+│   └── chunk-001/          # Parquet files for episodes 100–144
+├── videos/
+│   ├── chunk-000/rgb/      # MP4 videos for episodes 0–99
+│   └── chunk-001/rgb/      # MP4 videos for episodes 100–144
+├── meta/
+│   ├── info.json           # LeRobot dataset config
+│   └── stats.json          # Column statistics (min/max/mean/std)
+└── README.md
+```
 
 ---
 
-## Usage
+## Quick start
 
 ### With LeRobot
 
@@ -109,12 +140,13 @@ from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 
 dataset = LeRobotDataset("DynamicIntelligence/humanoid-robots-training-dataset")
 
-episode = dataset[0]
-state = episode["observation.camera_pose"]    # [6] camera 6-DoF
-task = episode["language_instruction"]        # "Fold the t-shirt on the bed."
+sample = dataset[0]
+print(sample["language_instruction"])        # "Fold the t-shirt on the bed."
+print(sample["observation.camera_pose"])     # tensor of shape [6]
+print(sample["action.left_hand_delta"])      # tensor of shape [9]
 ```
 
-### Direct Parquet Access
+### Direct download
 
 ```python
 import pandas as pd
@@ -123,20 +155,30 @@ from huggingface_hub import hf_hub_download
 path = hf_hub_download(
     repo_id="DynamicIntelligence/humanoid-robots-training-dataset",
     filename="data/chunk-000/episode_000000.parquet",
-    repo_type="dataset"
+    repo_type="dataset",
 )
 df = pd.read_parquet(path)
-print(df["language_instruction"].iloc[0])
+print(f"{len(df)} frames")
+print(df[["timestamp", "observation.camera_pose", "language_instruction"]].head())
 ```
 
 ---
 
+## Visualizer
+
+Browse episodes interactively:
+**[DI Hand Pose Sample Dataset Viewer](https://huggingface.co/spaces/DynamicIntelligence/dynamic_intelligence_sample_data)**
+
+The viewer shows the egocentric video alongside time-series plots of camera pose and hand positions, so you can see exactly what the person was doing and how the tracking data aligns with the video.
+
+---
+
 ## Citation
 
 ```bibtex
 @dataset{dynamic_intelligence_2025,
   author = {Dynamic Intelligence},
-  title = {Egocentric Human Motion Annotation Dataset},
+  title = {Humanoid Robot Training Dataset: Egocentric Hand Manipulation Demonstrations},
   year = {2025},
   publisher = {Hugging Face},
   url = {https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset}
@@ -147,11 +189,5 @@ print(df["language_instruction"].iloc[0])
 
 ## Contact
 
-**Email:** shayan@dynamicintelligence.company
 **Organization:** [Dynamic Intelligence](https://dynamicintelligence.company)
-
----
-
-## Visualizer
-
-Explore the dataset interactively: [DI Hand Pose Sample Dataset Viewer](https://huggingface.co/spaces/DynamicIntelligence/dynamic_intelligence_sample_data)
+**Email:** shayan@dynamicintelligence.company