v1.0

The release includes source code for OS-TOG, model weights, the database used for real-world experiments, and manually annotated object masks for the "UMD RGB-D Part Affordance Clutter" dataset.

Model Weights

• grasp_detection_model.pt - model weights for the grasp detection model, trained on the Cornell grasping dataset.
• instance_segmentation_model.pt - model weights for the instance segmentation model, trained on the OCID, UMD tools, and UMD clutter dataset.
• object_recognition_model.pt - model weights for the object recognition model, trained on the UMD tools dataset.

Data and Annotations

• UMD_clutter_annotations.pt - JSON annotation file that has object mask annotations for 30 scenes from the UMD clutter dataset that were manually annotated and used to train the instance segmentation model.
• OSTOG_physical_experiments.json - JSON annotation file of the objects and tasks the system knows that was used in real-world experiments.
• OSTOG_physical_experiments.zip - images of reference objects and their annotated affordance for the database created for real-world experiments.