GOAG：用于灵巧机器人操作的生成式与物体无关抓取规划器

摘要

多指抓取是一项至关重要的机器人技能，但当前的深度学习抓取规划器往往难以泛化到新物体，原因在于它们是在有限的、特定物体的数据集上训练的。我们提出了一种根本不同的方法，其基础在于观察到夹持器与物体在相互接触点处具有相同的表面几何形状。我们提出了GOAG：一种面向灵巧机器人操作的生成式与物体无关抓取规划器，其作为一种新型深度生成模型，学习特定夹持器接触表面分布的紧凑潜在表征，从而无需依赖特定物体的训练数据即可高效采样有效的抓取构型。我们表明，仅在推理时引入物体特征，我们的模型便能有效检索与夹持器能力兼容的可行接触区域。我们通过在仿真和真实场景中遵循既定抓取协议开展大量实验来验证我们的方法，并展示了该方法在文献中不同夹持器上的有效性。我们的方法在MultiDex数据集的物体上取得了最先进的结果，平均成功率达到86.93%。在生成大量抓取时，该方法提供显著更快的处理速度，同时与专门在该数据集上训练的领先方法性能相当。与这些方法不同，我们的方法不依赖特定物体的训练数据，从而凸显了物体无关学习的优势。它有效解决了传统数据驱动抓取规划器所面临的泛化挑战。代码和视频可在我们的项目网站上获取：https://cea-list.github.io/goagweb/ 。

English

Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-specific datasets. We introduce a fundamentally different approach, grounded in the observation that the gripper and the object share identical surface geometry at their mutual contact points. We propose GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation, a novel deep generative model that learns a compact latent representation of a specific gripper's contact surface distribution, enabling the efficient sampling of valid grasp configurations without relying on object-specific training data. We show that by introducing object features only at inference time, our model can effectively retrieve admissible contact areas that are compatible with the gripper's capabilities. We validate our approach through extensive experiments on established grasp protocols in both simulated and real-world scenarios, demonstrating its effectiveness with different grippers from the literature. Our method delivers state-of-the-art results on the objects from the MultiDex dataset, achieving an average success rate of 86.93%. It offers significantly faster processing when generating numerous grasps, while matching the performance of leading approaches specifically trained on this dataset. Unlike these methods, our approach does not rely on object-specific training data, highlighting the advantages of object-agnostic learning. It effectively addresses the generalization challenges faced by traditional data-driven grasp planners. Code and videos are available on our project website https://cea-list.github.io/goagweb/ .