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README.md

@@ -1,11 +1,9 @@
 
 # RoboChallenge Dataset
+## Tasks and Embodiments
+The dataset includes 30 diverse manipulation tasks (Table30) across 4 embodiments:
 
-## Dataset Structure
 ### Available Tasks
-
-The dataset includes 30 diverse manipulation tasks (Table30):
-
 - `arrange_flowers`
 - `arrange_fruits_in_basket` 
 - `arrange_paper_cups` 
@@ -36,7 +34,14 @@ The dataset includes 30 diverse manipulation tasks (Table30):
 - `turn_on_light_switch` 
 - `water_potted_plant` 
 - `wipe_the_table` 
+### Embodiments
+- **ARX5** - Single-arm with triple camera setup (wrist + global + right-side views)
+- **UR5** - Single-arm with dual camera setup (wrist + global views)
+- **FRANKA** - Single-arm with triple perspective setup (wrist + main + side views)
+- **ALOHA** - Dual-arm with triple wrist camera setup (left wrist + right wrist + global views)
+
 
+## Dataset Structure
 ### Hierarchy
 The dataset is organized by tasks, with each task containing multiple demonstration episodes:
 ```
@@ -50,17 +55,34 @@ The dataset is organized by tasks, with each task containing multiple demonstrat
 │       │   ├── meta/
 │       │   │   └── episode_meta.json    # Episode metadata
 │       │   ├── states/
-│       │   │   └── states.jsonl         # Robot states
+│       │   │   # for single-arm (ARX5, UR5, Franka)
+│       │   │   ├── states.jsonl         # Single-arm robot states
+│       │   │   # for dual-arm (ALOHA)
+│       │   │   ├── left_states.jsonl    # Left arm states
+│       │   │   └── right_states.jsonl   # Right arm states
 │       │   └── videos/
-│       │       ├── arm_realsense_rgb.mp4      # Arm-mounted camera
-│       │       ├── global_realsense_rgb.mp4   # Global view camera
-│       │       └── right_realsense_rgb.mp4    # Right-side camera (BEV)
+│       │       # Video configurations vary by robot model:
+│       │       # ARX5
+│       │       ├── arm_realsense_rgb.mp4       # Wrist view 
+│       │       ├── global_realsense_rgb.mp4    # Global view 
+│       │       └── right_realsense_rgb.mp4     # Side view
+│       │       # UR5
+│       │       ├── global_realsense_rgb.mp4    # Global view 
+│       │       └── handeye_realsense_rgb.mp4   # Wrist view
+│       │       # Franka
+│       │       ├── handeye_realsense_rgb.mp4   # Wrist view
+│       │       ├── main_realsense_rgb.mp4      # Global view 
+│       │       └── side_realsense_rgb.mp4      # Side view 
+│       │       # ALOHA
+│       │       ├── cam_high_rgb.mp4            # Global view 
+│       │       ├── cam_wrist_left_rgb.mp4      # Left wrist view
+│       │       └── cam_wrist_right_rgb.mp4     # Right wrist view
 │       ├── episode_000001/
 │       └── ...
 ├── convert_to_lerobot.py           # Conversion script
 └── README.md
 ```
-### JSON File Format
+### Metadata Schema
 `task_info.json`
 ```json
 {
@@ -96,20 +118,68 @@ The dataset is organized by tasks, with each task containing multiple demonstrat
 }
 ```
 
+### Robot States Schema
+Each episode contains states data stored in JSONL format. Depending on the embodiment, the structure differs slightly:
+- **Single-arm robots (ARX5, UR5, Franka)** → `states.jsonl`  
+- **Dual-arm robots (ALOHA)** → `left_states.jsonl` and `right_states.jsonl`  
+
+Each file records the robot’s proprioceptive signals per frame, including joint angles, 
+end-effector poses, gripper states, and timestamps.  The exact field definitions and coordinate conventions vary by platform, 
+as summarized below.
+
+#### ARX5
+| Data Name | Data Key |Shape | Semantics |
+|:---------:|:-----:|:----:|:----:|
+| Joint control |joint_positions | (6,) | Joint angle (in radians) from the base to the end effector. |
+| Pose control | ee_positions | (6,) | End effector pose (tx, ty, tz, roll, pitch, yaw), where (roll, pitch, yaw) is relative euler angles from the arm base coordinate. X : back to front; Y: right to left; Z: down to up. |
+| Gripper control |gripper | (1,) | Actual gripper width measurement in meter. | 
+| Time stamp |timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. |
+
+#### UR5
+| Data Name | Data Key |Shape | Semantics |
+|:---------:|:-----:|:----:|:----:|
+| Joint control |joint_positions | (6,) | Joint angle (in radians) from the base to the end effector. |
+| Pose control | ee_positions | (7,) | End effector pose (tx, ty, tz, rx, ry, rz, rw), where (tx, ty, tz) is relative position from the arm base coordinate , (rx, ry, rz, rw) is quaternion rotation. X : front to back; Y: left to right; Z: down to up. |
+| Gripper control |gripper | (1,) | Gripper closing angle, 0 for fully open, 255 for fully closed. | 
+| Time stamp |timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. | 
+
+#### Franka
+| Data Name | Data Key |Shape | Semantics |
+|:---------:|:-----:|:----:|:----:|
+| Joint control |joint_positions | (7,) | Joint angle (in radians) from the base to the end effector. |
+| Pose control | ee_positions | (7,) | End effector pose (tx, ty, tz, rx, ry, rz, rw), where (tx, ty, tz) is relative position from the arm base coordinate , (rx, ry, rz, rw) is quaternion rotation. X : back to front; Y: right to left; Z: down to up. |
+| Gripper control |gripper | (2,) | Gripper trigger signals in the (close_button, open_button) order. |
+| Gripper width |gripper_width | (1,) | Actual gripper width measurement |
+| Time stamp |timestamp | (1,) | Floating point timestamp (in milliseconds) of each frame. |
+
+
+#### ALOHA
+| Data Name | Data Key |Shape | Semantics |
+|:---------:|:-----:|:----:|:----:|
+| Master joint control |joint_positions | (6,) | Maste joint angle (in radians) from the base to the end effector. |
+|Joint  velocity| joint_vel | (7,) | Speed of 6 joint and gripper |
+| Puppet joint control |qpos | (6,) | Puppet joint angle (in radians) from the base to the end effector. |
+| Puppet pose control | ee_pose_quaternion | (7,) | End effector pose (tx, ty, tz, rx, ry, rz, rw), where (tx, ty, tz) is relative position from the arm base coordinate , (rx, ry, rz, rw) is quaternion rotation. X : back to front; Y: right to left ; Z: down to up. |
+| Puppet pose control | ee_pose_rpy | (6,) | End effector pose (tx, ty, tz, rr, rp, ry), where (tx, ty, tz) is relative position from the arm base coordinate , (rr, rp, ry) is euler (in radians). X : back to front; Y: right to left ; Z: down to up. |
+| Gripper control |gripper | (1,) |  Actual gripper width measurement in meter.|
+| Time stamp |timestamp | (1,) | Floating point timestamp (in mileseconds) of each frame. |
+
+
+## Convert to LeRobot
 
-## Convert to Lerobot
 While you can implement a custom Dataset class to read RoboChallenge data directly, **we strongly recommend converting to LeRobot format** to take advantage of [LeRobot](https://github.com/huggingface/lerobot)'s comprehensive data processing and loading utilities.
-The `convert_to_lerobot.py` script we provided generates a ready-to-use [LeRobot](https://github.com/huggingface/lerobot) dataset repository from RoboChallenge dataset.
+
+The example script **`convert_to_lerobot.py`** converts **ARX5** data to the LeRobot dataset as a example. For other robot embodiments (UR5, Franka, ALOHA), you can adapt the script accordingly.
 
 ### Prerequisites
 - Python 3.9+ with the following packages:
-  - `lerobot`
+  - `lerobot==0.1.0`
   - `opencv-python`
   - `numpy`
 - Configure `$LEROBOT_HOME`  (defaults to `~/.lerobot` if unset).
 
 ```bash
-pip install lerobot opencv-python numpy
+pip install lerobot==0.1.0 opencv-python numpy
 export LEROBOT_HOME="/path/to/lerobot_home"
 ```
 
@@ -125,5 +195,5 @@ python convert_to_lerobot.py \
 ```
 
 ### Output
-- Frames and metadata are saved to $LEROBOT_HOME/<repo-name>.
-- At the end, the script calls dataset.consolidate(run_compute_stats=False). If you require aggregated statistics, run it with run_compute_stats=True or execute a separate stats job.
+- Frames and metadata are saved to `$LEROBOT_HOME/<repo-name>`.
+- At the end, the script calls `dataset.consolidate(run_compute_stats=False)`. If you require aggregated statistics, run it with `run_compute_stats=True` or execute a separate stats job.