AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity. Leveraging powerful 3D generative models and vision foundation models (VFMs), the proposed AffordGen framework overcomes this limitation by utilizing the semantic correspondence of meaningful keypoints across large-scale 3D meshes to generate new robot manipulation trajectories. This large-scale, affordance-aware dataset is then used to train a robust, closed-loop visuomotor policy, combining the semantic generalizability of affordances with the reactive robustness of end-to-end learning. Experiments in simulat...
| Authors | Jiawei Zhang, Kaizhe Hu, Yingqian Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 April 2026 |
| Last updated at source | 30 May 2026 |
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