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Datasets:
charlesxu0124
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functional-manipulation-benchmark

Tasks:
Robotics
Languages:
English
ArXiv:
Tags:
Robotics
License:
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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    DataFilesNotFoundError
Message:      No (supported) data files found in charlesxu0124/functional-manipulation-benchmark
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response
                  get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1840, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1821, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1215, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 591, in infer_module_for_data_files
                  raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
              datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in charlesxu0124/functional-manipulation-benchmark

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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 sheet
    • color: color ID of the object according to reference 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
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