论文标题
gendexgrasp：可推广的灵巧抓握
GenDexGrasp: Generalizable Dexterous Grasping
论文作者
论文摘要
产生灵巧的抓握一直是一项长期且具有挑战性的机器人任务。尽管最近取得了进展，但现有方法主要遇到两个问题。首先，大多数先前的艺术都集中在特定类型的机器人手上，缺乏可概括地处理看不见的能力。其次，先前的艺术通常无法以很高的成功率迅速产生多样化的掌握。为了通过统一的解决方案共同解决这些挑战，我们提出了GendexGrasp，这是一种新型的手动握把算法，用于概括。 GendexGrasp对我们提出的大规模多手抓握数据集多端训练，该数据集由力量闭合优化合成。通过利用接触图作为手动无形的中间表示，GendexGrasp有效地产生了具有很高成功率的多样化和合理的握把姿势，并且可以在多种多指的机器人手中转移。与以前的方法相比，GendexGrasp在成功率，推理速度和多样性之间取得了三个方向的权衡。代码可在https://github.com/tengyu-liu/gendexgrasp上找到。
Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior arts focus on a specific type of robot hand, lacking the generalizable capability of handling unseen ones. Second, prior arts oftentimes fail to rapidly generate diverse grasps with a high success rate. To jointly tackle these challenges with a unified solution, we propose GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable grasping. GenDexGrasp is trained on our proposed large-scale multi-hand grasping dataset MultiDex synthesized with force closure optimization. By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands. Compared with previous methods, GenDexGrasp achieves a three-way trade-off among success rate, inference speed, and diversity. Code is available at https://github.com/tengyu-liu/GenDexGrasp.
