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license: apache-2.0
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# HORA: Hand–Object to Robot Action Dataset

## Dataset Summary

**HORA (Hand–Object to Robot Action)** is a large-scale **multimodal** dataset that converts human **hand–object interaction (HOI)** demonstrations into **robot-usable supervision** for cross-embodiment learning. It combines HOI-style annotations (e.g., MANO hand parameters, object pose, contact) with embodied-robot learning signals (e.g., robot observations, end-effector trajectories) under a unified canonical action space.

HORA is constructed from **three sources/subsets**:

1. **HORA(Mocap)**: custom multi-view motion capture system with **tactile-sensor gloves** (includes tactile maps).
2. **HORA(Recordings)**: custom RGB(D) HOI recording setup (no tactile).
3. **HORA(Public Dataset)**: derived from multiple public HOI datasets and retargeted to robot embodiments (6/7-DoF arms).

Overall scale: **~150k trajectories** across all subsets.

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## Key Features

- **Unified multimodal representation** across subsets, covering both HOI analysis and downstream robotic learning.
- **HOI modalities**: MANO hand parameters (pose/shape + global transform), object 6DoF pose, object assets, hand–object contact annotations.
- **Robot modalities**: wrist-view & third-person observations, and end-effector pose trajectories for robotic arms, all mapped to a canonical action space.
- **Tactile** (mocap subset): dense tactile map for both hand and object (plus object pose & assets).

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## Dataset Statistics

| Subset | Tactile | #Trajectories | Notes |
| --- | --- | --- | --- |
| HORA(Mocap) | ✅ | 63,141 | 6-DoF object pose + assets + tactile map |
| HORA(Recordings) | ❌ | 23,560 | 6-DoF object pose + assets |
| HORA(Public Dataset) | ❌ | 66,924 | retargeted cross-embodiment robot modalities |
| **Total** |  | **~150k** |  |

## Supported Tasks and Use Cases

HORA is suitable for:

- **Imitation Learning (IL)** / **Visuomotor policy learning**
- **Vision–Language–Action (VLA)** model training and evaluation
- HOI-centric research: contact analysis, pose/trajectory learning, hand/object dynamics


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## Data Format



### Example Episode Structure

Each episode/trajectory may include:

**HOI fields**

- `hand_mano`: MANO parameters (pose/shape, global rotation/translation)
- `object_pose_6d`: 6DoF object pose sequence
- `contact`: hand–object contact annotations
- `object_asset`: mesh/texture id or path

**Robot fields**

- **Global Attributes**
  - `task_description`: Natural language instruction for the task (stored as HDF5 attribute).
  - `total_demos`: Total number of trajectories in the file.

- **Observations** (`obs` group)
  - `agentview_rgb`: JPEG byte stream (variable length `uint8`). Decodes to `(T, 480, 640, 3)`.
  - `eye_in_hand_{side}_rgb`: JPEG byte stream (variable length `uint8`). Decodes to `(T, 480, 640, 3)`.
  - `{prefix}_joint_states`: Arm joint positions in radians. Shape `(T, N_dof)`.
  - `{prefix}_gripper_states`: Gripper joint positions. Shape `(T, N_grip)`.
  - `{prefix}_eef_pos`: End-effector position in **Robot Base Frame**. Shape `(T, 3)`.
  - `{prefix}_eef_quat`: End-effector orientation `(w, x, y, z)` in **Robot Base Frame**. Shape `(T, 4)`.
  - `object_{name}_pos`: Object ground truth position in **World Frame**. Shape `(T, 3)`.
  - `object_{name}_quat`: Object ground truth orientation `(w, x, y, z)` in **World Frame**. Shape `(T, 4)`.

- **Actions & States**
  > **Note:** For multi-robot setups, the fields below concatenate data from all robots in order (e.g., `[robot0, robot1]`).
  - `actions`: Joint-space control targets. Shape `(T, N_dof + 1)`.
    *Format: `[joint_positions, normalized_gripper]` where gripper is in `[0, 1]`.*
  - `actions_ee`: Cartesian control targets. Shape `(T, 7)`.
    *Format: `[pos (3), axis-angle (3), normalized_gripper (1)]`.*
  - `robot_states`: Robot base pose in **World Frame**. Shape `(T, 7 * N_robots)`.
    *Format: `[pos (3), quat (4)]` per robot, quat is `(w, x, y, z)`.*

**Tactile fields (mocap only)**

- `tactile_hand`: dense tactile map (time × sensors/vertices)
- `tactile_object`: dense tactile map


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