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FMB: A Functional Manipulation Benchmark for Generalizable Robotic Learning
FMB: A Functional Manipulation Benchmark for Generalizable Robotic Learning
Jianlan Luo*,
Charles Xu*,
Fangchen Liu,
Liam Tan,
Zipeng Lin,
Jeffrey Wu,
Pieter Abbeel, and
Sergey Levine
* Equal Contribution
The International Journal of Robotics Research (IJRR) 2024
Our benchmark for studying robotic learning for functional manipulation consists of a variety of easily
reproducible 3D printed objects, each one requiring a sequence of grasping, reorientation, and assembly
behaviors. Generalization can be evaluated on test objects and varied positions, as well as more complex
multi-stage assembly tasks. We also provide an imitation learning system that provides a basic set of
policies for each skill, allowing researchers to use our tasks as a toolkit for studying any portion of the
pipeline.
Dataset Overview
Our dataset consists of objects in diverse appearance and geometry. It requires multi-stage
and multi-modal fine motor skills to successfully assemble the pegs onto a unfixed board in
a randomized scene. We collected a total of 22,550 trajectories across two different tasks on
a Franka Panda arm. We record the trajectories from 2 global views and 2 wrist views. Each view
contains both RGB and depth map.
Visit the dataset page for more detail and links to download the dataset.
Visit the dataset page for more detail and links to download the dataset.
Single-Object Multi-Stage Manipulation Task
The Single-Object Multi-Stage Manipulation Task consists of 54 different assembly objects and 3 assembly
boards of various shapes, sizes, and colors. The robot has to grasp the object, perform a series of
reorientation actions with an environment fixture, then insert into the board. The time horizon for this
task ranges from 20 to 40 seconds. To make the task managable, we break down each
end-to-end trajectory into seperate primitives such as grasp, place on fixture, regrasp, rotate, move to
board, and insert. Examples of each trajectories can be visualized below.
View a Random Trajectory
Sample
Side 1 View
Side 2 View
Wrist 1 View
Wrist 2 View
loading...
Multi-Object Multi-Stage Manipulation Task
The Multi-Object Multi-Stage Manipulation Task consists of 3 sets of assembly object. Each set contains 4
interlocking objects that need to be assembled together sequentially using the same primitives as the
previous task. The time horizon for this task is even longer, and can easily exceed 100 seconds to fully
assemble one board. Examples of each trajectories can be visualized below.
View a Random Trajectory
Sample
Side 1 View
Side 2 View
Wrist 1 View
Wrist 2 View
loading...
Reproducing the Benchmark
All the objects in our benchmark are designed to be 3D printable. We provide the CAD files for all the
objects and links to material needed in the Material and
CAD Files section.
A detailed description on how to setup the workspace can be found in the Workspace Setup section.
We also provide a detailed description of the evaluation procedure in the Evaluation Procedure section.
A detailed description on how to setup the workspace can be found in the Workspace Setup section.
We also provide a detailed description of the evaluation procedure in the Evaluation Procedure section.
Others Using FMB
We are excited to see the community is using FMB for their research. If you are using FMB in your work, please let us know by emailing functionalmanipulationdataset@gmail.com and we would love to feature your work here.
Sun, Jiankai, et al. "ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly."