通过三种接触方式提升触觉形状重建效率，减少34%交互次数。
Grasp, Slide, Roll: Comparative Analysis of Contact Modes for Tactile-Based Shape Reconstruction
- 对比抓取、滑动和滚动三种接触模式，优化触觉采样策略。
- 滑动与滚动使重建速度提升34%，准确率提高55%。
- 适合需要高效触觉感知的机器人抓取任务研究者。
触觉传感通过物理交互可获取物体详细几何信息，弥补视觉方法的不足。然而，由于物理接触耗时且需合理选择采样位置以最大化信息量，高效获取有用触觉数据仍具挑战。本文研究不同接触模式对触觉驱动形状重建的影响，使用灵巧夹爪对比三种交互方式：抓取-释放、手指擦过引发的滑动、手掌滚动。结合基于信息论的探索框架与形状补全模型，指导后续采样位置。实验表明，手指擦过与手掌滚动显著提升触觉采集效率，使形状重建收敛速度加快34%，准确率提升55%。在配备Inspire-Robots灵巧手的UR5e机械臂上验证，对各类基本几何物体均表现稳健。
原文摘要 · Abstract (English)
Tactile sensing allows robots to gather detailed geometric information about objects through physical interaction, complementing vision-based approaches. However, efficiently acquiring useful tactile data remains challenging due to the time-consuming nature of physical contact and the need to strategically choose contact locations that maximize information gain while minimizing physical interactions. This paper studies how different contact modes affect object shape reconstruction using a tactile-enabled dexterous gripper. We compare three contact interaction modes: grasp-releasing, sliding induced by finger-grazing, and palm-rolling. These contact modes are combined with an information-theoretic exploration framework that guides subsequent sampling locations using a shape completion model. Our results show that the improved tactile sensing efficiency of finger-grazing and palm-rolling translates into faster convergence in shape reconstruction, requiring 34% fewer physical interactions while improving reconstruction accuracy by 55%. We validate our approach using a UR5e robot arm equipped with an Inspire-Robots Dexterous Hand, showing robust performance across primitive object geometries.
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