--- license: cc-by-4.0 task_categories: - robotics tags: - LeRobot - fmb - manipulation - franka - force-torque - contact-rich size_categories: - 1M **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 ## 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). This port converts each single-object demonstration into **one LeRobot episode**, keeping the RGB streams, proprioception, force/torque, and action on a uniform frame grid. - **Episodes:** 1844 - **Frames:** 418,495 @ 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*, *rotate*) - **Scope:** single-object subset only (FMB's multi-object subset is not included in this port). ## 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 object metadata (shape/size/length/color/angle/distractor + `object_info`) is in `meta/fmb_episodes.json`. ## Fidelity notes (please read) - **Depth dropped.** FMB's 4 depth maps are **not** included in this port (RGB + F/T + proprio + action only). Use the original dataset if you need depth. - **BGR → RGB.** FMB stores images in BGR; they are 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, so `fps` is metadata, not a resampling rate. ## 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