---
license: cc-by-4.0
language:
- en
pretty_name: FMB
task_categories:
- robotics
tags:
- Robotics
---
# Functional Manipulation Benchmark

This robot learning dataset is a part of the paper "FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning". It includes 22,550 expert
demonstration trajectories across different skills required to solve the Single-Object and Multi-Object Manipulation Tasks presented in the paper.


Link to paper: https://arxiv.org/abs/2401.08553

Link to website: https://functional-manipulation-benchmark.github.io



## Dataset Structure
Each zip file contains a folder of trajectories. Each trajectory is saved as a .npy file. Each .npy file contains a dictionary with the following key-value pairs:

  - `obs/side_1`: a (N, 256, 256, 3) numpy array of RGB images from the side camera 1 saved in BGR format
  - `obs/side_2`: a (N, 256, 256, 3) numpy array of RGB images from the side camera 2 saved in BGR format
  - `obs/wrist_1`: a (N, 256, 256, 3) numpy array of RGB images from the wrist camera 1 saved in BGR format
  - `obs/wrist_2`: a (N, 256, 256, 3) numpy array of RGB images from the wrist camera 2 saved in BGR format
  - `obs/side_1_depth`: a (N, 256, 256) numpy array of depth images from the side camera 1
  - `obs/side_2_depth`: a (N, 256, 256) numpy array of depth images from the side camera 2
  - `obs/wrist_1_depth`: a (N, 256, 256) numpy array of depth images from the wrist camera 1
  - `obs/wrist_2_depth`: a (N, 256, 256) numpy array of depth images from the wrist camera 2
  - `obs/tcp_pose`: a (N, 7) numpy array of the end effector pose in the robot's base frame (XYZ, Quaternion)
  - `obs/tcp_vel`: a (N, 6) numpy array of the end effector velocity in the robot's base frame (XYZ, RPY)
  - `obs/tcp_force`: a (N, 3) numpy array of the end-effector force in the robot's end-effector frame (XYZ)
  - `obs/tcp_torque`: a (N, 3) numpy array of the end-effector torque in the robot's end-effector frame (RPY)
  - `obs/q`: a (N, 7) numpy array of the joint positions
  - `obs/dq`: a (N, 7) numpy array of the joint velocities
  - `obs/jacobian`: a (N, 6, 7) numpy array of the robot jacobian
  - `obs/gripper_pose`: a (N, ) numpy array indicating the binary state of the gripper (0=open, 1=closed)
  - `action`: a (N, 7) numpy array of the commanded cartesian action (XYZ, RPY, gripper)
  - `primitive`: a (N, ) numpy array of strings indicating the primitive associated with the current timestep
  - `object_id` (Multi-Object only): a (N, ) numpy array of integers indicating the ID of the object being manipulated in the current trajectory
  - `object_info` (Single-Object only): a dictionary containing information of the object being manipulated in the current trajectory with the following keys-value pairs:
    - `length`: length of the object (S=Short, L=Long)
    - `size`: cross-sectional size of the object (S=Small, M=Medium, L=Large)
    - `shape`: shape ID of the object according to [reference](https://functional-manipulation-benchmark.github.io/static/doc/FMB%20Shape%20and%20Color%20Number%20Reference%20Sheet%20-%20Google%20Docs.pdf) sheet
    - `color`: color ID of the object according to [reference](https://functional-manipulation-benchmark.github.io/static/doc/FMB%20Shape%20and%20Color%20Number%20Reference%20Sheet%20-%20Google%20Docs.pdf) sheet
    - `angle`: initial pose of the object indicating how it should be grasped and reoriented (horizontal, vertical)
    - `distractor`: indicator for whether there are distractor objects (y=yes, n=no)
## File Naming
The Single-Object Dataset trajectory files are named as follows:

    (insert_only_){shape}_{size}_{length}_{color}_{angle}_{distractor}_{trajectory_id}.npy

The Multi-Object Dataset trajectory files are named as follows:

    trajectory_{object_id}_{trajectory_id}.npy