Dexterous Grasp Transformer
CVPR 2024(2024)
摘要
In this work, we propose a novel discriminative framework for dexterous grasp
generation, named Dexterous Grasp TRansformer (DGTR), capable of predicting a
diverse set of feasible grasp poses by processing the object point cloud with
only one forward pass. We formulate dexterous grasp generation as a set
prediction task and design a transformer-based grasping model for it. However,
we identify that this set prediction paradigm encounters several optimization
challenges in the field of dexterous grasping and results in restricted
performance. To address these issues, we propose progressive strategies for
both the training and testing phases. First, the dynamic-static matching
training (DSMT) strategy is presented to enhance the optimization stability
during the training phase. Second, we introduce the adversarial-balanced
test-time adaptation (AB-TTA) with a pair of adversarial losses to improve
grasping quality during the testing phase. Experimental results on the
DexGraspNet dataset demonstrate the capability of DGTR to predict dexterous
grasp poses with both high quality and diversity. Notably, while keeping high
quality, the diversity of grasp poses predicted by DGTR significantly
outperforms previous works in multiple metrics without any data pre-processing.
Codes are available at https://github.com/iSEE-Laboratory/DGTR .
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要