GOAG：適用於靈巧機器人操作的生成式且物件無關之抓取規劃器

摘要

多指抓取是一項關鍵的機器人技能，但當前的深度學習抓取規劃器常因訓練於有限且特定物體的資料集，而難以泛化至新物體。我們提出一種根本不同的方法，其基礎在於觀察到夾爪與物體在相互接觸點上具有相同的表面幾何。我們提出 GOAG：適用於靈巧機器人操作的生成式且與物體無關的抓取規劃器（Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation），這是一種新穎的深度生成模型，學習特定夾爪接觸表面分布的緊湊潛在表示，從而能在不依賴特定物體訓練資料的情況下，高效取樣有效的抓取構型。我們證明，僅在推論時引入物體特徵，我們的模型便能有效檢索與夾爪能力相容的可行接觸區域。我們透過在模擬與真實世界場景中，針對既有抓取協議進行廣泛實驗來驗證我們的方法，並展示其在不同文獻中的夾爪上的有效性。我們的方法在 MultiDex 資料集的物體上達到最先進的結果，平均成功率為 86.93%。它在生成大量抓取時提供顯著更快的處理速度，同時匹配專門在此資料集上訓練的領先方法的效能。與這些方法不同，我們的方法不依賴特定物體的訓練資料，凸顯了與物體無關學習的優勢。它有效解決了傳統資料驅動抓取規劃器所面臨的泛化挑戰。程式碼與影片可在我們的專案網站取得：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/ .