Abstract
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. 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 project website.
Pipeline Overview
Simulation Experiments
We evaluate our teacher and student policies and compare them with GraspReward-only and Multi-state singulation baselines. The evaluation metrics are success rates denoted by SR, and average steps denoted by AS. We denote dense and random arrangement as D-n and R-n respectively, where n is the number of obstacles.
To test generalization beyond cuboid clutters, we evaluate our policy on tightly packed irregular clutters formed by slicing a cuboid with random curves. After fine-tuning on 200 cases, we test on 50 unseen ones and achieve satisfactory results compared with baselines.
Discussion on Dexterity
We evaluate three LEAP Hand variants—Low, Mid, and Full DoF—on dense and irregular clutters. As dexterity increases from 6 to 16 DoF, grasp success improves significantly, highlighting the importance of high-DoF hands in tight clutter scenarios. Note that (F) stands for Flexion & Extension DoF, (A) stands for Abduction & Adduction DoF. One single arrow represents one DoF in the Figure.
Real-World Experiments
We evaluate our policy on D-8, R-8, irregular, and practical clutters with 10 real-world trials per setting, where success is defined as lifting the target by 10 cm within 40 seconds. Despite no extensive sim-to-real adaptation, the policy shows strong performance, though affected by dynamic interactions and sim-to-real discrepancies. Notably, the R-8 policy achieves 60% success on practical clutters in a zero-shot setting, demonstrating robust generalization to unseen object shapes and spatial configurations.
with 8 obstacles
with 8 obstacles
We tested on more diverse practical clutters as shown below.
