1CFCS, Peking University
2School of EECS, Peking University
3Beijing Academy of Artificial Intelligence
* equal contributions
† corresponding author
We introduce a large-scale cross-category part manipulation benchmark PartManip with diverse object datasets, realistic settings, and rich annotations. We propose a generalizable vision-based policy learning strategy and boost the performance of part-based object manipulation by a large margin, which can generalize to unseen object categories and novel objects in the real world.
Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes. Parts, as the shared components in different object categories, have the potential to increase the generaliza- tion ability of the manipulation policy and achieve cross- category object manipulation. In this work, we build the first large-scale, part-based cross-category object manip- ulation benchmark, PartManip, which is composed of 11 object categories, 494 objects, and 1432 tasks in 6 task classes. Compared to previous work, our benchmark is also more diverse and realistic, i.e., having more objects and using sparse-view point cloud as input without oracle information like part segmentation. To tackle the difficul- ties of vision-based policy learning, we first train a state- based expert with our proposed part-based canonicaliza- tion and part-aware rewards, and then distill the knowledge to a vision-based student. We also find an expressive back- bone is essential to overcome the large diversity of different objects. For cross-category generalization, we introduce domain adversarial learning for domain-invariant feature extraction. Extensive experiments in simulation show that our learned policy can outperform other methods by a large margin, especially on unseen object categories. We also demonstrate our method can successfully manipulate novel objects in the real world.
An Overview of Our Domain-generalizable Part Segmentation and Pose Estimation Method. We introduce a part-oriented domain adversarial training strategy that can tackle multi-resolution features and distribution imbalance for the domain-invariant GAPart feature extraction. The training strategy tackles the challenges in our tasks and dataset, significantly improving the generalizability of our method for part segmentation and pose estimation.
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