Bi-Adapt: 通过语义对应实现少样本双臂适应于3D物体新类别
Bi-Adapt: Few-shot Bimanual Adaptation for Novel Categories of 3D Objects via Semantic Correspondence
AI总结：
Bi-Adapt通过语义对应实现双臂操作在新3D物体类别中的少样本适应，利用视觉基础模型实现跨类别的 affordance 映射，有效提升泛化能力。
摘要：
双臂操作对于机器人执行复杂任务至关重要，但具有挑战性，需要两个手臂之间的协调合作。然而，现有的双臂操作方法往往依赖于昂贵的数据收集和训练，难以高效地泛化到新类别的未见物体。在本文中，我们提出了Bi-Adapt，一种新的框架，旨在通过语义对应实现双臂操作的高效泛化。Bi-Adapt通过利用视觉基础模型的强大能力，实现跨类别的 affordance 映射。通过在新类别上使用有限的数据进行微调，Bi-Adapt在零样本情况下表现出显著的泛化能力。在仿真和现实环境中的广泛实验验证了我们方法的有效性，并展示了其高效率，能够在有限数据下实现不同基准任务在新类别中的高成功率。项目网站：https://biadapt-project.github.io/
英文摘要：
Bimanual manipulation is imperative yet challenging for robots to execute complex tasks, requiring coordinated collaboration between two arms. However, existing methods for bimanual manipulation often rely on costly data collection and training, struggling to generalize to unseen objects in novel categories efficiently. In this paper, we present Bi-Adapt, a novel framework designed for efficient generalization for bimanual manipulation via semantic correspondence. Bi-Adapt achieves cross-category affordance mapping by leveraging the strong capability of vision foundation models. Fine-tuning with restricted data on novel categories, Bi-Adapt exhibits notable generalization to out-of-category objects in a zero-shot manner. Extensive experiments conducted in both simulation and real-world environments validate the effectiveness of our approach and demonstrate its high efficiency, achieving a high success rate on different benchmark tasks across novel categories with limited data. Project website: https://biadapt-project.github.io/
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。
