AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence

TL;DR

AffordGen leverages 3D generative and vision foundation models to create diverse, affordance-aware demonstrations, enhancing robot manipulation generalization and data efficiency.

Contribution

It introduces a novel framework that uses semantic keypoints and generative models to produce diverse demonstrations for improved robot learning.

Findings

Policies trained with AffordGen achieve high success rates.

Zero-shot generalization to unseen objects is enabled.

Significant improvement in data efficiency for robot learning.

Abstract

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 simulation and the real world show that policies trained with AffordGen achieve high success rates and enable zero-shot generalization to truly unseen objects, significantly improving data efficiency in robot learning.