README.md

---
license: cc-by-4.0
task_categories:
- robotics
tags:
- LeRobot
- fmb
- manipulation
- franka
- force-torque
- contact-rich
- assembly
size_categories:
- 100K<n<1M
---

# FMB multi-object (LeRobot v3)

A LeRobot Dataset v3 port of the **FMB** (Functional Manipulation Benchmark)
**multi-object manipulation** demonstrations (assembly boards), recorded with a Franka Panda arm.

> **This is a reformatted derivative**, not the original release. The original data and
> full documentation are published by the authors:
> **https://huggingface.co/datasets/charlesxu0124/functional-manipulation-benchmark**
> Paper: [arXiv:2401.08553](https://arxiv.org/abs/2401.08553) · Project: https://functional-manipulation-benchmark.github.io

> Single-object counterpart: **[`robot-lev/fmb`](https://huggingface.co/datasets/robot-lev/fmb)**.

## What this is

FMB ships one `.npy` per demonstration (4 RGB + 4 depth cameras, proprioception, 6-axis
end-effector force/torque, a commanded cartesian action, and per-step skill primitives).
The multi-object subset spans **three assembly boards** (`board_1`, `board_2`, `board_3`).
This port converts each demonstration into **one LeRobot episode**, keeping the RGB streams,
proprioception, force/torque, and action on a uniform frame grid.

- **Episodes:** 1804 (board_1: 600, board_2: 600, board_3: 604)
- **Frames:** 338,188 @ 10 fps
- **Robot:** Franka Panda
- **Cameras:** `side_1`, `side_2`, `wrist_1`, `wrist_2` (RGB 256×256)
- **Per-frame task:** the active skill primitive (e.g. *grasp*, *insert*, *regrasp*, *place_on_fixture*)

## Features

| key | dtype | shape | notes |
|---|---|---|---|
| `observation.images.{side_1,side_2,wrist_1,wrist_2}` | video | 256×256×3 | RGB (converted from FMB's BGR) |
| `observation.state` | float32 | (28,) | joint pos (7) + joint vel (7) + EE pose (7) + EE vel (6) + gripper (1) |
| `observation.state.joint_position` | float32 | (7,) | |
| `observation.state.ee_pose` | float32 | (7,) | xyz + quaternion, base frame |
| `observation.state.gripper` | float32 | (1,) | 0=open, 1=closed |
| `observation.force` | float32 | (3,) | end-effector force, **EE frame** |
| `observation.torque` | float32 | (3,) | end-effector torque, **EE frame** |
| `observation.jacobian` | float32 | (42,) | robot jacobian (6×7), flattened |
| `action` | float32 | (7,) | commanded cartesian: xyz, rpy, gripper |

Per-episode metadata (`board`, `object_id`, `trajectory_id`, `primitives`) is in
`meta/fmb_episodes.json`.

## Fidelity notes (please read)

- **Depth dropped.** FMB's 4 depth maps are **not** included (RGB + F/T + proprio + action only).
- **BGR → RGB.** FMB stores images in BGR; converted to RGB here.
- **Action is the FMB commanded action as-is** (no next-pose reconstruction).
- **fps = 10 is nominal.** The source `.npy` carry no timestamps; frames map 1:1.

## Citation

```bibtex
@article{luo2024fmb,
  title   = {FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning},
  author  = {Luo, Jianlan and Xu, Charles and Liu, Fangchen and Tan, Liam and Lin, Zipeng and Wu, Jeffrey and Abbeel, Pieter and Levine, Sergey},
  journal = {arXiv preprint arXiv:2401.08553},
  year    = {2024}
}
```

Conversion scripts: https://github.com/lvjonok/fmb-lerobot-port