提出分步抓取算法，让机械手更稳更快抓多个物体。
Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation
- 分步生成多物体抓取姿态，仅用部分机械手自由度。
- 抓取成功率比现有方法高8.71%至43.33%。
- 适合需要快速、稳定多物抓取的机器人场景。
我们提出序列式多物体灵巧抓取采样算法SeqGrasp，可利用机械手的部分自由度，鲁棒地合成多种物体上的稳定抓取。基于SeqGrasp构建了大规模Allegro手序列抓取数据集SeqDataset，用于训练基于扩散模型的序列抓取生成器SeqDiffuser。在仿真和真实机器人上对SeqGrasp与SeqDiffuser进行实验评估，结果表明其抓取成功率较当前最先进的非序列多物体抓取方法MultiGrasp提升8.71%至43.33%。此外，SeqDiffuser生成抓取的速度比SeqGrasp和MultiGrasp快约1000倍。
原文摘要 · Abstract (English)
We introduce the sequential multi-object robotic grasp sampling algorithm SeqGrasp that can robustly synthesize stable grasps on diverse objects using the robotic hand's partial Degrees of Freedom (DoF). We use SeqGrasp to construct the large-scale Allegro Hand sequential grasping dataset SeqDataset and use it for training the diffusion-based sequential grasp generator SeqDiffuser. We experimentally evaluate SeqGrasp and SeqDiffuser against the state-of-the-art non-sequential multi-object grasp generation method MultiGrasp in simulation and on a real robot. The experimental results demonstrate that SeqGrasp and SeqDiffuser reach an 8.71%-43.33% higher grasp success rate than MultiGrasp. Furthermore, SeqDiffuser is approximately 1000 times faster at generating grasps than SeqGrasp and MultiGrasp. Project page: https://yulihn.github.io/SeqGrasp/.
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