Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping

Abstract

The integration of optimization 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 outperform previous datasets in grasp diversity, scene diversity, modality diversity. Our grasp generation method has demonstrated remarkable success, outperforming established baselines with 81.1% success rate in real-world single-object grasping and 75.3% success rate in multi-object grasping.

Multi-modal FFH Cluttered Grasping Dataset

FFH Grasping Poses from Cluttered Scenes