---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- franka
- teleoperation
- real-robot
pretty_name: object_classification
---

# object_classification

Real-robot teleoperation demonstrations of the `object_classification` task on a single-arm Franka Research 3 cell,
released in four LeRobot layouts. Every layout is a conversion of the same 80 raw episodes
(28,569 frames at 10 Hz); the layouts differ only in the LeRobot codebase version and in the
action representation.

| directory | LeRobot version | action (`action`) | consumer |
|---|---|---|---|
| `lerobot_v21_abs_joint/` | v2.1 | 8-D absolute joint targets + gripper | RLDX-1 loader (`episodes.jsonl`, `meta/modality.json`) |
| `lerobot_v21_delta_eef/` | v2.1 | 7-D end-effector velocity command + gripper | RLDX-1 loader |
| `lerobot_v30_abs_joint/` | v3.0 | 8-D absolute joint targets + gripper | `lerobot` ≥ 0.5 (`LeRobotDataset`), e.g. π0.5 |
| `lerobot_v30_delta_eef/` | v3.0 | 7-D end-effector velocity command + gripper | `lerobot` ≥ 0.5 |

The v3.0 layouts were derived from the v2.1 layouts with the official `lerobot` converter
(`convert_dataset_v21_to_v30.py`), so the four directories carry identical frames, states and videos.

## Task

> Place each object from the tray into the drawer, board, or container that contains its matching object.

Every frame carries the task instruction in the `task` field (one task per dataset) and a `subtask` string
column that holds the same instruction (reserved for subtask-level labels).

## Raw data and conversion

Source: DROID-style teleoperation captures (one `teleoperation.h5` per episode, HDF5) recorded with a
Meta Quest controller driving the arm through a cartesian-velocity controller at a nominal 10 Hz.
Each capture stores, per control step, the measured robot state (`joint_positions`, `gripper_position`,
`cartesian_position`, joint velocities and torques), the commanded actions in several spaces
(`joint_position`, `cartesian_velocity`, `cartesian_position`, `gripper_position`, …), controller
flags, per-camera timestamps and intrinsics, and the camera streams as embedded mp4 bytes
(two ZED stereo cameras, left and right eye each, 1280×720).

Conversion rules applied identically to all four layouts:

| field | source in the capture | value |
|---|---|---|
| `observation.state` (8) | `/observation/robot_state/joint_positions` (7), `gripper_position` (1) | measured joint angles (rad) + measured gripper closedness (0 = open) |
| `action` (abs_joint, 8) | `/action/joint_position` (7), `/action/gripper_position` (1) | commanded absolute joint targets (rad) + commanded gripper closedness in [0, 1] |
| `action` (delta_eef, 7) | `/action/cartesian_velocity` (6), `/action/gripper_position` (1) | commanded cartesian velocity (vx vy vz wx wy wz, controller units in [-1, 1]) + commanded gripper closedness |
| `observation.image.exterior_camera` | exterior ZED 2i, left eye | 448×252 RGB video (AV1, `libsvtav1` crf 30), 10 fps |
| `observation.image.wrist_camera` | wrist ZED Mini, left eye | 448×252 RGB video (AV1), 10 fps |
| `timestamp`, `frame_index`, `episode_index`, `index`, `task_index` | control-step index | standard LeRobot bookkeeping |

Only the left eye of each stereo pair is kept; frames are resized from 1280×720 to 448×252 (same aspect
ratio, no crop). The gripper is a Robotiq 2F; the `state` vector is the same in both action layouts, only
the action head differs. The v2.1 layouts include `meta/modality.json` (state `arm` 0:7, `gripper` 7:8;
action `arm` 0:7 / `gripper` 7:8 for abs_joint, `eef_velocity` 0:6 / `gripper` 6:7 for delta_eef) and
`meta/stats.json` with the quantile statistics used for normalisation.

## Statistics

| | value |
|---|---|
| episodes | 80 |
| frames | 28,569 |
| fps | 10 |
| episode length | 311–418 steps (mean 357) |
| cameras | 2 (exterior, wrist), 448×252 |
| robot type | `franka_panda` (Franka Research 3 arm, Robotiq 2F gripper) |

## Loading

```python
# v3.0 layouts (lerobot >= 0.5)
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("Myungkyu/object_classification", root="<local copy>/lerobot_v30_abs_joint")
```

```bash
# any layout, plain download
huggingface-cli download Myungkyu/object_classification --repo-type dataset --include "lerobot_v21_abs_joint/*" --local-dir .
```

Companion checkpoints trained on these layouts: `Myungkyu/hiwrld-baseline-ckpts-real-robot`.