The integration of optimization method and generative models has
significantly advanced dexterous manipulation techniques for five-fingered hand
grasping. Yet, the application of these techniques in cluttered environments is
a relatively unexplored area. To address this research gap, we have developed a
novel method for generating five-fingered hand grasp samples in cluttered
settings. This method emphasizes simulated grasp quality and the nuanced
interaction between the hand and surrounding objects. A key aspect of our
approach is our data generation method, capable of estimating contact spatial
and semantic representations and affordance grasps based on object affordance
information. Furthermore, our Contact Semantic Conditional Variational
Autoencoder (CoSe-CVAE) network is adept at creating comprehensive contact maps
from point clouds, incorporating both spatial and semantic data. We introduce a
unique grasp detection technique that efficiently formulates mechanical hand
grasp poses from these maps. Additionally, our evaluation model is designed to
assess grasp quality and collision probability, significantly improving the
practicality of five-fingered hand grasping in complex scenarios. Our data
generation method outperforms previous datasets in grasp diversity, scene
diversity, modality diversity. Our grasp generation method has demonstrated
remarkable success, outperforming established baselines with 81.0% average
success rate in real-world single-object grasping and 75.3% success rate in
multi-object grasping. The dataset and supplementary materials can be found at
this https URL, and we will release the
code upon publication.

将优化方法与生成模型相结合，我们开发了一个新颖的方法来在杂乱的环境中生成五指抓取样本，并通过评估模型改进了抓取质量和碰撞概率，显著提高了在复杂情境中进行五指抓取的实用性。