MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping

• URL: http://arxiv.org/abs/2507.02672v1
• Date: Thu, 03 Jul 2025 14:36:45 GMT
• Title: MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping
• Authors: Qingyu Fan, Yinghao Cai, Chao Li, Chunting Jiao, Xudong Zheng, Tao Lu, Bin Liang, Shuo Wang,
• Abstract summary: MISCGrasp is a volumetric grasping method that integrates multi-scale feature extraction with contrastive feature enhancement for self-adaptive grasping.<n>We propose a query-based interaction between high-level and low-level features through the Insight Transformer, while the Empower Transformer selectively attends to the highest-level features.<n>Extensive experiments in both simulated and real-world environments demonstrate that MISCGrasp outperforms baseline and variant methods in tabletop decluttering tasks.
• Score: 15.127239823566194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic grasping faces challenges in adapting to objects with varying shapes and sizes. In this paper, we introduce MISCGrasp, a volumetric grasping method that integrates multi-scale feature extraction with contrastive feature enhancement for self-adaptive grasping. We propose a query-based interaction between high-level and low-level features through the Insight Transformer, while the Empower Transformer selectively attends to the highest-level features, which synergistically strikes a balance between focusing on fine geometric details and overall geometric structures. Furthermore, MISCGrasp utilizes multi-scale contrastive learning to exploit similarities among positive grasp samples, ensuring consistency across multi-scale features. Extensive experiments in both simulated and real-world environments demonstrate that MISCGrasp outperforms baseline and variant methods in tabletop decluttering tasks. More details are available at https://miscgrasp.github.io/.

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