DexSinGrasp：在密集杂乱环境中学习灵巧物体分离与抓取的统一策略
DexSinGrasp: Learning a Unified Policy for Dexterous Object Singulation and Grasping in Densely Cluttered Environments
AI总结：
提出DexSinGrasp统一策略，利用灵巧手高自由度在密集杂乱环境中高效分离并抓取物体，通过课程学习与策略蒸馏提升成功率与泛化性，实验优于基线。
摘要：
在杂乱环境中抓取物体仍然是机器人操作中一个基本但具有挑战性的问题。尽管先前的工作已经探索了基于学习的推抓协同策略用于两指夹爪，但很少有工作利用灵巧手的高自由度（DoF）来在杂乱环境中执行高效分离以辅助抓取。在本工作中，我们提出了DexSinGrasp，一种用于灵巧物体分离和抓取的统一策略。DexSinGrasp实现了高灵巧性的物体分离以促进抓取，显著提高了杂乱环境中的效率和有效性。我们引入了杂乱排列课程学习以提升成功率并增强在不同杂乱条件下的泛化能力，同时通过策略蒸馏实现了可部署的基于视觉的抓取策略。为评估我们的方法，我们引入了一系列具有不同物体排列和遮挡水平的杂乱抓取任务。实验结果表明，我们的方法在效率和抓取成功率上均优于基线方法，尤其在密集杂乱环境中表现突出。代码、附录和视频可在我们的网站 https://nus-lins-lab.github.io/dexsingweb/ 获取。
英文摘要：
Grasping objects in cluttered environments remains a fundamental yet challenging problem in robotic manipulation. While prior works have explored learning-based synergies between pushing and grasping for two-fingered grippers, few have leveraged the high degrees of freedom (DoF) in dexterous hands to perform efficient singulation for grasping in cluttered settings. In this work, we introduce DexSinGrasp, a unified policy for dexterous object singulation and grasping. DexSinGrasp enables high-dexterity object singulation to facilitate grasping, significantly improving efficiency and effectiveness in cluttered environments. We incorporate clutter arrangement curriculum learning to enhance success rates and generalization across diverse clutter conditions, while policy distillation enables a deployable vision-based grasping strategy. To evaluate our approach, we introduce a set of cluttered grasping tasks with varying object arrangements and occlusion levels. Experimental results show that our method outperforms baselines in both efficiency and grasping success rate, particularly in dense clutter. Codes, appendix, and videos are available on our website https://nus-lins-lab.github.io/dexsingweb/.
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。
