用灵巧手在密集杂物中高效分离并抓取物体
DexSinGrasp: Learning a Unified Policy for Dexterous Object Singulation and Grasping in Densely Cluttered Environments
- 统一策略同时实现灵巧手的分离与抓取
- 密集杂乱环境下抓取成功率显著提升
- 适合需要高精度操作的工业场景
在密集杂乱环境中抓取物体仍是机器人操作中的基础性难题。尽管已有研究探索了双指夹爪在推移与抓取间的协同学习，但很少利用灵巧手的高自由度来实现杂乱环境下的高效分离抓取。本文提出DexSinGrasp，一种统一的灵巧手分离与抓取策略。该方法通过引入杂乱排列课程学习，提升在多种杂乱条件下的成功率与泛化能力；结合策略蒸馏，实现可部署的视觉驱动抓取方案。为评估方法性能，我们构建了一组具有不同物体排列和遮挡程度的杂乱抓取任务。实验结果表明，本方法在效率和抓取成功率上均优于基线，尤其在密集杂乱场景下表现突出。
原文摘要 · Abstract (English)
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/.
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