CVPR 2023 – GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

In this episode we discuss GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts
by Haoran Geng, Helin Xu, Chengyang Zhao, Chao Xu, Li Yi, Siyuan Huang, He Wang. The paper proposes a method called Generalizable and Actionable Parts (GAParts) for learning cross-category domain-generalizable object perception and manipulation. This involves defining 9 GAPart classes to construct a part-centric interactive dataset named GAPartNet with rich part-level annotations for over 8,000 part instances on 1,166 objects. The authors investigate three cross-category tasks and propose a robust 3D segmentation method that integrates adversarial learning techniques to address domain gaps between seen and unseen object categories and manipulation heuristics that generalize well to unseen object categories in both the simulator and the real world.