AffordGen: 通过可供性对应生成多样化演示以实现通用物体操作
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
AI总结：
提出AffordGen框架，利用3D生成模型和视觉基础模型在大规模3D网格上的语义对应生成多样化操作轨迹，训练鲁棒的闭环视觉运动策略，实现零样本泛化到未见物体。
摘要：
尽管现代模仿学习方法在机器人操作中取得了近期成功，但其性能常常受到数据多样性不足导致的几何变化的限制。利用强大的3D生成模型和视觉基础模型（VFMs），所提出的AffordGen框架通过利用大规模3D网格上有意义关键点的语义对应来生成新的机器人操作轨迹，从而克服了这一限制。然后，这个大规模、可供性感知的数据集被用于训练一个鲁棒的、闭环的视觉运动策略，结合了可供性的语义泛化能力和端到端学习的反应性鲁棒性。在仿真和现实世界中的实验表明，使用AffordGen训练的策略实现了高成功率，并能够零样本泛化到真正未见过的物体，显著提高了机器人学习中的数据效率。项目页面：https://jiaweiz9.github.io/AffordGen-release/
英文摘要：
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. Project Page: https://jiaweiz9.github.io/AffordGen-release/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
