Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: mit
+license: cc-by-4.0
 task_categories:
 - robotics
 ---
@@ -11,11 +11,10 @@ task_categories:
 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 to find solutions to the tasks automatically [1]. 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.
 
 
-This dataset is for research and development only.
-
-## Dataset Owner(s):
+## Dataset Contact(s):
 Fabio Ramos ([email protected]) <br>
 Anqi Li ([email protected])
 
@@ -23,31 +22,65 @@ Anqi Li ([email protected])
 03/18/2025
 
 ## License/Terms of Use: 
-MIT
+cc-by-4.0
 
 ## Intended Usage:
-This dataset is provided in LeRobot format and is intended for training robot policies.
+This dataset is provided in LeRobot format and is intended for training robot policies and foundation models.
 
 ## Dataset Characterization
-** Data Collection Method<br>
-* [Automated] <br>
-* [Automatic/Sensors] <br>
-* [Synthetic] <br>
+* Data Collection Method<br>
+  * [Automated] <br>
+  * [Automatic/Sensors] <br>
+  * [Synthetic] <br>
 
-** Labeling Method<br>
-* [Automated] <br>
-* [Automatic/Sensors] <br>
-* [Synthetic] <br>
+* Labeling Method<br>
+  * [Synthetic] <br>
 
 ## Dataset Format
-[Insert Modality and Format Classification]
+Within the collection, there are three datasets in LeRobot format `pick`, `place_bench`, and `place_cabinet`. 
+* `pick`: The robot picks an object from the bench top. <br>
+* `place bench`: The robot starts with the object at the gripper and places it on the kitchen's bench top.
+* `place cabinet`: The robot starts with the object at the gripper and places it inside an opened cabinet.  
+
+The videos below show three examples of the tasks: 
+
+<div style="display: flex; justify-content: flex-start;">
+<img src="./assets/episode_000028.gif" width="300" height="300" alt="pick" />
+<img src="./assets/episode_000008.gif" width="300" height="300" alt="place_bench" />
+<img src="./assets/episode_000048.gif" width="300" height="300" alt="place_cabinet" />
+</div>
+
+* 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 the different camera modalities for a single trajectory. 
+<div style="display: flex; justify-content: flex-start;">
+<img src="./assets/episode_000057.gif" width="300" height="300" alt="rgb" />
+<img src="./assets/episode_000057_depth.gif" width="300" height="300" alt="depth" />
+<img src="./assets/episode_000057_semantic.gif" width="300" height="300" alt="semantic" />
+</div>
 
 
 ## Dataset Quantification
-
-[Insert Record Count- Note number of records (images, video, audio files, etcetera)]
-[Insert Feature Count- Note features present in record count above (e.g. tags)]
-[Insert Measurement of Total Data Storage]
+Record Count:
+* `pick`
+  * number of episodes: 272
+  * number of frames: 69726
+  * number of videos: 4080 (1360 RGB videos, 1360 depth videos, 1360 semantic segmentation videos)
+* `place bench`
+  * number of episodes: 142
+  * number of frames: 29728
+  * number of videos: 2130 (710 RGB videos, 710 depth videos, 710 semantic segmentation videos)
+* `place cabinet`
+  * number of episodes: 126
+  * number of frames: 30322
+  * number of videos: 1890 (630 RGB videos, 630 depth videos, 630 semantic segmentation videos)
+Total storage: 4.0 GB
 
 
 ## Reference(s):
@@ -75,4 +108,4 @@ This dataset is provided in LeRobot format and is intended for training robot po
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.   
 
 (For Release on NVIDIA Platforms Only)
-Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).