---
task_categories:
  - robotics
tags:
  - robot
  - manipulation
  - navigation
  - whole-body
  - teleoperation
  - bimanual
  - mobile-manipulation
  - G2
pretty_name: "RoboChallenge ICRA Whole Body Control Dataset"
size_categories:
  - 1K<n<10K
---

# RoboChallenge ICRA Whole Body Control Dataset

This dataset contains **2,800 teleoperated episodes** collected on the [Agibot G2](https://www.agibot.com/) humanoid robot. Each episode captures a full mobile manipulation sequence—navigating to a shelf, grasping drink(s), walking to a cart, and placing item(s) into it—recorded via multi-camera observations and full-body proprioception at **30 Hz**.

## Tasks

| Task | Episodes | Description |
|------|----------|-------------|
| `grasp_the_drink` | 1,278 | Navigate to the shelf, grasp **one** drink with one hand, carry it to the cart, and place it in. |
| `grasp_the_drink_with_both_hands` | 1,522 | Navigate to the shelf, grasp **two** drinks (one per hand), carry them to the cart, and place them in. |

### Drink Variants

Episodes cover **16 drink categories**: ENONE Juice, Itoen Barley Tea, Schweppes Vitamin C, 7UP, Mirinda, Coconut Water, Frozen Pear Drink, JDB Herbal Tea, Pepsi, Huayang Soda, Weichuan Daily C, Aojiru, Tropicana, Minute Maid Juice, Coca Cola,ENONE Sparkling Water.

---

## Directory Structure

```
{task_name}/
├── meta.json
└── data/
    └── episode_XXXXX/
        ├── meta/
        │   └── task_desc.json
        ├── states/
        │   └── aligned_joints.h5
        └── videos/
            ├── head_color.mp4
            ├── head_depth.mp4
            ├── head_stereo_left_color.mp4
            ├── head_stereo_right_color.mp4
            ├── hand_left_color.mp4
            └── hand_right_color.mp4
```

- **`meta.json`** — Task-level metadata (sits alongside the `data/` folder).
- **`data/episode_XXXXX/`** — One episode, zero-padded index (`episode_00000`, `episode_00001`, …).
  - **`meta/task_desc.json`** — Episode-level task description, including natural-language prompts in English and Chinese, scoring rubric, and task tags.
  - **`states/aligned_joints.h5`** — Per-frame aligned proprioception, action commands, and camera timestamps (HDF5).
  - **`videos/`** — Six MP4 camera streams (see [Cameras](#cameras) below).

---

## Cameras

Each episode contains **six** video streams (fisheye cameras are excluded from this release):

| File | View | Resolution | Codec | FPS |
|------|------|------------|-------|-----|
| `head_color.mp4` | Head RGB | 640 × 400 | HEVC | 30 |
| `head_depth.mp4` | Head depth (encoded as video) | 640 × 400 | PNG | 30 |
| `head_stereo_left_color.mp4` | Head stereo left RGB | 1920 × 1536 | HEVC | 30 |
| `head_stereo_right_color.mp4` | Head stereo right RGB | 1920 × 1536 | HEVC | 30 |
| `hand_left_color.mp4` | Left wrist / hand RGB | 1280 × 1056 | HEVC | 30 |
| `hand_right_color.mp4` | Right wrist / hand RGB | 1280 × 1056 | HEVC | 30 |

### Synchronization

For each integer frame `i` in `aligned_joints.h5`, the group `f"{i}"/timestamp/camera/<name>` stores a `uint64` array of length 1 representing the sensor timestamp. Use these timestamps to align decoded video frames to HDF5 rows. Available timestamp keys: `hand_left_color`, `hand_right_color`, `head_color`, `head_depth`, `head_stereo_left`, `head_stereo_right`.

---

## `aligned_joints.h5` — Proprioception & Actions

### Top Level

The HDF5 root contains one child group per aligned sample, named by **string** keys `"0"`, `"1"`, …, `"N-1"`. **Sort by integer value** (not lexicographic order) when iterating.

### Per-Frame Layout (`{i}/`)

| Entry | Type | Description |
|-------|------|-------------|
| `main_timestamp` | `uint64` scalar | Primary timeline stamp for the row |
| `action/` | Group | Commanded / policy-side targets |
| `state/` | Group | Measured robot state |
| `timestamp/` | Group | Per-sensor capture times (`timestamp/camera/…`) |

### `action/`

| Path | Shape | dtype | Semantics |
|------|-------|-------|-----------|
| `action/joint/position` | `(14,)` | `float64` | Commanded arm joint positions (rad); 7 left + 7 right |
| `action/left_effector/position` | `(1,)` | `float64` | Commanded left gripper (rad) |
| `action/right_effector/position` | `(1,)` | `float64` | Commanded right gripper (rad) |
| `action/waist/position` | `(5,)` | `float64` | Commanded body/waist joint positions (rad) |
| `action/head/position` | `(3,)` | `float64` | Commanded head joint positions (rad) |
| `action/end/position` | `(2, 3)` | `float64` | Commanded end-effector positions (m); row 0=left, 1=right |
| `action/end/orientation` | `(2, 4)` | `float64` | Commanded EE orientations as quaternions `(x, y, z, w)` |
| `action/robot/velocity` | `(6,)` | `int64` | Chassis / mobile base command |

### `state/`

| Path | Shape | dtype | Semantics |
|------|-------|-------|-----------|
| `state/joint/position` | `(14,)` | `float64` | Measured arm joints (rad) |
| `state/joint/velocity` | `(14,)` | `float64` | Arm joint rates |
| `state/joint/effort` | `(14,)` | `float64` | Arm joint torques |
| `state/joint/mode` | `(14,)` | `uint32` | Per-joint mode flags |
| `state/head/position` | `(3,)` | `float64` | Measured head joints (rad) |
| `state/head/velocity` | `(3,)` | `float64` | Head joint rates |
| `state/head/effort` | `(3,)` | `float64` | Head efforts |
| `state/head/mode` | `(3,)` | `uint32` | Head mode flags |
| `state/waist/position` | `(5,)` | `float64` | Measured waist joints (rad) |
| `state/waist/velocity` | `(5,)` | `float64` | Waist rates |
| `state/waist/effort` | `(5,)` | `float64` | Waist efforts |
| `state/waist/mode` | `(5,)` | `uint32` | Waist mode flags |
| `state/robot/position` | `(3,)` | `float64` | Measured base pose |
| `state/robot/orientation` | `(4,)` | `float64` | Base orientation (quaternion) |
| `state/end/position` | `(2, 3)` | `float64` | Measured EE positions |
| `state/end/orientation` | `(2, 4)` | `float64` | Measured EE quaternions |
| `state/end/arm_position` | `(2, 3)` | `float64` | Arm-frame positions |
| `state/end/arm_orientation` | `(2, 4)` | `float64` | Arm-frame orientations |
| `state/end/pose` | `(28,)` | `float64` | Flattened pose vector |
| `state/end/velocity` | `(24,)` | `float64` | End-state velocities |
| `state/end/wrench` | `(24,)` | `float64` | Wrench / F-T vector |
| `state/end/mode` | `(1,)` | `uint32` | EE controller mode |
| `state/end/errcode` | `(1,)` | `uint32` | Error code |
| `state/end/errmsg` | `(1,)` | `object` | Textual error (variable-length) |
| `state/left_effector/position` | `(1,)` | `float64` | Measured left gripper |
| `state/right_effector/position` | `(1,)` | `float64` | Measured right gripper |

---

## `task_desc.json`

Each episode's `meta/task_desc.json` contains a JSON object with:

| Key | Description |
|-----|-------------|
| `task_name` | Task identifier string (e.g. `grasp_the_drink`) |
| `prompt` | Object with a `text` template and variable slots (e.g. `{drink}`, `{drink1}`, `{drink2}`) plus a `drink` list of possible values |
| `description` | Short natural-language task summary |
| `scoring` | Rubric string describing scoring breakdown |
| `task_tags` | List of tags (`grasping`, `manipulation`, `navigation`, `whole_body`, `G2`) |

---

## Joint Limits (radians)

Hardware / URDF joint limits for the G2A platform:

<details>

<summary><b>Body (waist) — 5 joints</b></summary>

| Joint | Min | Max |
|-------|-----|-----|
| `idx01_body_joint1` | −1.0821 | 0.0002 |
| `idx02_body_joint2` | −0.0002 | 2.6529 |
| `idx03_body_joint3` | −1.9199 | 1.5710 |
| `idx04_body_joint4` | −0.4363 | 0.4363 |
| `idx05_body_joint5` | −3.0456 | 3.0456 |

</details>

<details>
<summary><b>Head — 3 joints</b></summary>

| Joint | Min | Max |
|-------|-----|-----|
| `idx11_head_joint1` | −1.5710 | 1.5710 |
| `idx12_head_joint2` | −0.3492 | 0.3492 |
| `idx13_head_joint3` | −0.5348 | 0.5348 |

</details>

<details>
<summary><b>Left arm — 7 joints</b></summary>

| Joint | Min | Max |
|-------|-----|-----|
| `idx21_arm_l_joint1` | −3.0718 | 3.0718 |
| `idx22_arm_l_joint2` | −2.0595 | 2.0595 |
| `idx23_arm_l_joint3` | −3.0718 | 3.0718 |
| `idx24_arm_l_joint4` | −2.4958 | 1.0123 |
| `idx25_arm_l_joint5` | −3.0718 | 3.0718 |
| `idx26_arm_l_joint6` | −1.0123 | 1.0123 |
| `idx27_arm_l_joint7` | −1.5359 | 1.5359 |

</details>

<details>
<summary><b>Right arm — 7 joints</b></summary>

| Joint | Min | Max |
|-------|-----|-----|
| `idx61_arm_r_joint1` | −3.0718 | 3.0718 |
| `idx62_arm_r_joint2` | −2.0595 | 2.0595 |
| `idx63_arm_r_joint3` | −3.0718 | 3.0718 |
| `idx64_arm_r_joint4` | −2.4958 | 1.0123 |
| `idx65_arm_r_joint5` | −3.0718 | 3.0718 |
| `idx66_arm_r_joint6` | −1.0123 | 1.0123 |
| `idx67_arm_r_joint7` | −1.5359 | 1.5359 |

</details>

<details>
<summary><b>End effectors (grippers)</b></summary>

| Joint | Min | Max |
|-------|-----|-----|
| `idx31_gripper_l_inner_joint1` | −0.91 | 0 |
| `idx71_gripper_r_inner_joint1` | −0.91 | 0 |

</details>

---

## Robot Platform

- **Model**: Agibot G2A — a full-size humanoid with mobile base, 5-DOF waist, 3-DOF head, dual 7-DOF arms, and parallel-jaw grippers.
- **End Effectors**: Listed per-episode in `meta_info.json` → `ee_list` (e.g. `zhiyuan_gripper_omnipicker`).
- **Collection**: Teleoperation (task mode `TDC`).
- **Frequency**: 30 Hz aligned proprioception; video streams at native sensor rate, synchronized via timestamps.

---

## Quick Start

```python
import h5py
import json
from pathlib import Path

task_root = Path("grasp_the_drink")
episode_dir = task_root / "data" / "episode_00000"

# 1. Read task description
with open(episode_dir / "meta" / "task_desc.json") as f:
    task_desc = json.load(f)
print(task_desc["grasp_the_drink"]["description"])

# 2. Load proprioception
with h5py.File(episode_dir / "states" / "aligned_joints.h5", "r") as f:
    frame_keys = sorted(f.keys(), key=int)
    print(f"Number of frames: {len(frame_keys)}")

    # Read frame 0
    frame = f[frame_keys[0]]
    action_joints = frame["action/joint/position"][:]      # (14,)
    state_joints  = frame["state/joint/position"][:]       # (14,)
    left_gripper  = frame["state/left_effector/position"][:] # (1,)
    print(f"Action joints shape: {action_joints.shape}")
    print(f"State joints shape:  {state_joints.shape}")

# 3. Video files
video_dir = episode_dir / "videos"
for mp4 in sorted(video_dir.glob("*.mp4")):
    print(mp4.name)
```

---

## Citation

If you use this dataset, please cite:

```bibtex
@misc{dexmal_g2_icra_dataset,
  title   = {RoboChallenge ICRA Whole Body Control Dataset},
  author  = {Dexmal},
  year    = {2026},
  url     = {https://huggingface.co/datasets/RoboChallenge/icra_wbc}
}
```