Update readme

README.md

@@ -3,16 +3,17 @@ tags:
 - robotics
 - grasping
 - simulation
-license: "cc-by-4.0"
+- nvidia
+license: "cc-by-nc-4.0"
 ---
 
-# GraspGen: Scaling Simulated Grasping
-GraspGen is a large-scale simulated grasp dataset for multiple robot embodiments and grippers 
+# GraspGen: Scaling Sim2Real Grasping
+GraspGen is a large-scale simulated grasp dataset for multiple robot embodiments and grippers.
 
 <img src="assets/cover.png" width="1000" height="250" title="readme1"> 
 
 
-We release over 57 million grasps, computed for a subset of 8515 objects from the [Objaverse XL](https://objaverse.allenai.org/) (LVIS) dataset. We release grasps for three grippers: Franka Panda, the Robotiq-2f-140 industrial gripper, and suction. 
+We release over 57 million grasps, computed for a subset of 8515 objects from the [Objaverse XL](https://objaverse.allenai.org/) (LVIS) dataset. These grasps are specific to three grippers: Franka Panda, the Robotiq-2f-140 industrial gripper, and a single-contact suction gripper (30mm radius). 
 
 <img src="assets/montage2.png" width="1000" height="500" title="readme2"> 
 
@@ -28,20 +29,23 @@ splits/
 	robotiq2f140/{train/valid}_scenes.json
 	suction/{train/valid}_scenes.json
 ```
-We release test-train splits along with the grasp dataset. 
+We release test-train splits along with the grasp dataset. The splits are made randomly based on object instances.
 
 Each json file in the shard has the following data in a python dictionary. Note that `num_grasps=2000` per object.
 ```
 ‘object’/
-	‘scale’ # This is the scale of the asset
+	‘scale’ # This is the scale of the asset, float
 ‘grasps’/
 	‘object_in_gripper’ # boolean mask indicating grasp success, [num_grasps X 1]
 	‘transforms’ # Pose of the gripper in homogenous matrices, [num_grasps X 4 X 4]
 ```
 
+The coordinate frame convention for the three grippers are provided below:
+<img src="assets/grippers.png" width="450" height="220" title="readme3"> 
+
 ## Visualizing the dataset
 
-We have provided some standalone scripts for visualizing this dataset. See the header of the [visualize_dataset.py](scripts/visualize_dataset.py) for installation instructions
+We have provided some minimal, standalone scripts for visualizing this dataset. See the header of the [visualize_dataset.py](scripts/visualize_dataset.py) for installation instructions.
 
 Before running any of the visualization scripts, remember to start meshcat-server in a separate terminal:
 ``` shell
@@ -53,18 +57,21 @@ To visualize a single object from the dataset, alongside its grasps:
 cd scripts/ && python visualize_dataset.py --dataset_path /path/to/dataset --object_uuid {object_uuid} --object_file /path/to/mesh --gripper_name {choose from: franka, suction, robotiq2f140}
 ```
 
+To sequentially visualize a list of objects with its grasps:
+```shell
+cd scripts/ && python visualize_dataset.py --dataset_path /path/to/dataset --uuid_list {path to a splits.json file} --uuid_object_paths_file {path to a json file mapping uuid to absolute path of meshes} --gripper_name {choose from: franka, suction, robotiq2f140}
+```
+
 ## Objaverse dataset
 Please download the Objaverse XL (LVIS) objects separately. See the helper script [download_objaverse.py](scripts/download_objaverse.py) for instructions and usage.
+Note that running this script autogenerates a file that maps from `UUID` to the asset mesh path, which you can pass in as input `uuid_object_paths_file` to the `visualize_dataset.py` script.
 
 ## License
 License Copyright © 2025, NVIDIA Corporation & affiliates. All rights reserved.
 
-
-The dataset is released under a CC-BY 4.0 License.
-
-The visualization code is released under the [NVIDIA source code license](LICENSE).
+Both the dataset and visualization code is released under a CC-BY-NC 4.0 [License](LICENSE_DATASET).
 
 ## Contact
 
-Please reach out to [Adithya Murali](adithyamurali.com) ([email protected]) and [Clemens Eppner](https://clemense.github.io/) ([email protected]) for further enquiries
+Please reach out to [Adithya Murali](http://adithyamurali.com) ([email protected]) and [Clemens Eppner](https://clemense.github.io/) ([email protected]) for further enquiries.