nvidia/PhysicalAI-Robotics-Manipulation-Kitchen

PhysicalAI Robotics Manipulation in the Kitchen

Dataset Description:

PhysicalAI-Robotics-Manipulation-Kitchen is a dataset of automatic generated motions of robots performing operations such as opening and closing cabinets, drawers, dishwashers and fridges. The dataset was generated in IsaacSim leveraging reasoning algorithms and optimization-based motion planning to find solutions to the tasks automatically [1, 3]. The dataset includes a bimanual manipulator built with Kinova Gen3 arms. The environments are kitchen scenes where the furniture and appliances were procedurally generated [2]. This dataset is available for commercial use.

Dataset Contact(s)

Fabio Ramos ([email protected])
Anqi Li ([email protected])

Dataset Creation Date

03/18/2025

License/Terms of Use

cc-by-4.0

Intended Usage

This dataset is provided in LeRobot format and is intended for training robot policies and foundation models.

Dataset Characterization

• Data Collection Method
  

  • Automated
    
  • Automatic/Sensors
    
  • Synthetic
    

• Labeling Method
  

  • Synthetic
    

Dataset Format

Within the collection, there are eight datasets in LeRobot format open_cabinet, close_cabinet, open_dishwasher, close_dishwasher, open_fridge, close_fridge, open_drawerandclose_drawer`.

• open cabinet: The robot opens a cabinet in the kitchen.
  
• close cabinet: The robot closes the door of a cabinet in the kitchen.
  
• open dishwasher: The robot opens the door of a dishwasher in the kitchen.
  
• close dishwasher: The robot closes the door of a dishwasher in the kitchen.
  
• open fridge: The robot opens the fridge door.
  
• close fridge: The robot closes the fridge door.
  
• open drawer: The robot opens a drawer in the kitchen.
  
• close drawer: The robot closes a drawer in the kitchen.
  

The videos below illustrate three examples of the tasks:

• action modality: 34D which includes joint states for the two arms, gripper joints, pan and tilt joints, torso joint, and front and back wheels.
• observation modalities
  • observation.state: 13D where the first 12D are the vectorized transform matrix of the "object of interest". The 13th entry is the joint value for the articulated object of interest (i.e. drawer, cabinet, etc).
  • observation.image.world__world_camera: 512x512 images of RGB, depth and semantic segmentation renderings stored as mp4 videos.
  • observation.image.external_camera: 512x512 images of RGB, depth and semantic segmentation renderings stored as mp4 videos.
  • observation.image.world__robot__right_arm_camera_color_frame__right_hand_camera: 512x512 images of RGB, depth and semantic segmentation renderings stored as mp4 videos.
  • observation.image.world__robot__left_arm_camera_color_frame__left_hand_camera: 512x512 images of RGB, depth and semantic segmentation renderings stored as mp4 videos.
  • observation.image.world__robot__camera_link__head_camera: 512x512 images of RGB, depth and semantic segmentation renderings stored as mp4 videos.

The videos below illustrate three of the cameras used in the dataset.

Dataset Quantification

Record Count:

• open_cabinet
  • number of episodes: 78
  • number of frames: 39292
  • number of videos: 1170 (390 RGB videos, 390 depth videos, 390 semantic segmentation videos)
• close_cabinet
  • number of episodes: 205
  • number of frames: 99555
  • number of videos: 3075 (1025 RGB videos, 1025 depth videos, 1025 semantic segmentation videos)
• open_dishwasher
  • number of episodes: 72
  • number of frames: 28123
  • number of videos: 1080 (360 RGB videos, 360 depth videos, 360 semantic segmentation videos)
• close_dishwasher
  • number of episodes: 74
  • number of frames: 36078
  • number of videos: 1110 (370 RGB videos, 370 depth videos, 370 semantic segmentation videos)
• open_fridge
  • number of episodes: 193
  • number of frames: 93854
  • number of videos: 2895 (965 RGB videos, 965 depth videos, 965 semantic segmentation videos)
• close_fridge
  • number of episodes: 76
  • number of frames: 41894
  • number of videos: 1140 (380 RGB videos, 380 depth videos, 380 semantic segmentation videos)
• open_drawer
  • number of episodes: 99
  • number of frames: 37214
  • number of videos: 1485 (495 RGB videos, 495 depth videos, 495 semantic segmentation videos)
• close_drawer
  • number of episodes: 77
  • number of frames: 28998
  • number of videos: 1155 (385 RGB videos, 385 depth videos, 385 semantic segmentation videos)

Total storage: 11.4 GB

Reference(s)

[1] @inproceedings{garrett2020pddlstream,
    title={Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning},
    author={Garrett, Caelan Reed and Lozano-P{\'e}rez, Tom{\'a}s and Kaelbling, Leslie Pack},
    booktitle={Proceedings of the international conference on automated planning and scheduling},
    volume={30},
    pages={440--448},
    year={2020}
}

[2] @article{Eppner2024,
    title = {scene_synthesizer: A Python Library for Procedural Scene Generation in Robot Manipulation},
    author = {Clemens Eppner and Adithyavairavan Murali and Caelan Garrett and Rowland O'Flaherty and Tucker Hermans and Wei Yang and Dieter Fox},
    journal = {Journal of Open Source Software}
    publisher = {The Open Journal},
    year = {2024},
    Note = {\url{https://scene-synthesizer.github.io/}}
}

[3] @inproceedings{curobo_icra23,
    author={Sundaralingam, Balakumar and Hari, Siva Kumar Sastry and
        Fishman, Adam and Garrett, Caelan and Van Wyk, Karl and Blukis, Valts and
        Millane, Alexander and Oleynikova, Helen and Handa, Ankur and
        Ramos, Fabio and Ratliff, Nathan and Fox, Dieter},
    booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
    title={CuRobo: Parallelized Collision-Free Robot Motion Generation},
    year={2023},
    volume={},
    number={},
    pages={8112-8119},
    doi={10.1109/ICRA48891.2023.10160765}
}

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