让机器人稳定抓取各类变形物体，成功率超95%
D3Grasp: Diverse and Deformable Dexterous Grasping for General Objects
- 融合视觉与触觉的统一感知表示
- 真实世界测试成功率达95.1%，优于现有方法
- 适合需要灵活抓握的工业或服务场景
实现对通用且易变形物体的多样、稳定灵巧抓取仍是机器人领域的基础挑战，源于高维动作空间和感知不确定性。本文提出D3Grasp，一种多模态感知引导的强化学习框架，用于实现多样且可变形的灵巧抓取。首先，引入统一的多模态表征，整合视觉与触觉信息，以稳健抓取具有多样化属性的常见物体。其次，提出非对称强化学习架构，在训练中利用特权信息的同时保持部署真实性，提升泛化能力与样本效率。第三，精心设计训练策略，生成接触丰富、无穿透且运动学可行的抓取动作，增强对可变形及接触敏感物体的适应性。大量实验验证表明，D3Grasp在大规模多样物体类别上表现出高度鲁棒性，显著推动了可变形与柔性物体灵巧抓取的性能边界，即使在感知不确定性和现实干扰下仍表现优异。在真实世界测试中，平均成功率达到95.1%，优于刚性与可变形物体基准上的已有方法。
原文摘要 · Abstract (English)
Achieving diverse and stable dexterous grasping for general and deformable objects remains a fundamental challenge in robotics, due to high-dimensional action spaces and uncertainty in perception. In this paper, we present D3Grasp, a multimodal perception-guided reinforcement learning framework designed to enable Diverse and Deformable Dexterous Grasping. We firstly introduce a unified multimodal representation that integrates visual and tactile perception to robustly grasp common objects with diverse properties. Second, we propose an asymmetric reinforcement learning architecture that exploits privileged information during training while preserving deployment realism, enhancing both generalization and sample efficiency. Third, we meticulously design a training strategy to synthesize contact-rich, penetration-free, and kinematically feasible grasps with enhanced adaptability to deformable and contact-sensitive objects. Extensive evaluations confirm that D3Grasp delivers highly robust performance across large-scale and diverse object categories, and substantially advances the state of the art in dexterous grasping for deformable and compliant objects, even under perceptual uncertainty and real-world disturbances. D3Grasp achieves an average success rate of 95.1% in real-world trials,outperforming prior methods on both rigid and deformable objects benchmarks.
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