| --- |
| 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 |
|
|
