融合视觉与触觉信息，让机械手自适应调整抓取姿势。
AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion
- 通过关联触觉信号与手指身份，融合3D几何与触觉数据
- 在仿真和真实环境中抓取成功率显著提升
- 适合需要精细操作的机器人抓取任务
人类通过无缝整合视觉感知与触觉反馈实现稳定且自适应的抓握，这一能力在机器人系统中仍难以复现。现有机器人抓取方法主要依赖视觉输入，缺乏接触后的触觉引导适应机制，限制了鲁棒性和泛化能力。为此，我们提出一种统一的视觉-触觉融合抓取框架，集成抓取生成、可行性预测与自适应优化。核心在于引入高效视觉-触觉表示，将物体几何形状与触觉反馈紧密融合，并通过关联触觉信号与手指身份实现细粒度交互建模。该统一表示支持规划阶段的接触感知抓取姿态生成及接触后的触觉引导优化，使系统能够动态调整抓取策略。在仿真与真实环境中的全面实验表明，本方法显著提升了抓取成功率并增强了对多样化物体的泛化能力。
原文摘要 · Abstract (English)
Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback, a capability that remains challenging to replicate in robotic systems. Existing robotic grasping approaches predominantly rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact, limiting robustness and generalization. To address this challenge, we propose a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement. At its core, our method introduces an efficient visuo-tactile representation that tightly fuses object geometry with tactile feedback by associating tactile signals with finger identities. This unified representation supports contact-aware grasp pose generation during planning and tactile-guided refinement after contact, enabling the system to reason about fine-grained finger-object interactions and adjust grasps dynamically. Comprehensive experiments in both simulation and real-world environments demonstrate that our approach significantly enhances grasp success rates and generalization across diverse objects.
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