用点云感知预测抓取目标，提升柔性外骨骼控制精度
Point Cloud-based Grasping for Soft Hand Exoskeleton
- 基于点云几何建模实现环境感知与抓取预测
- 在15种物体上达91%抓取成功率，优于现有方法
- 无需大量标注数据，适合真实场景的柔性外骨骼控制
抓取是人与环境交互的基本技能，但手部功能障碍者难以完成。为辅助此类用户，柔性手部外骨骼可恢复或增强手部功能，但其控制因环境理解复杂而困难。本文提出一种基于视觉的预测控制框架，利用深度感知获得上下文信息，预测抓取目标并确定下一控制状态。该方法基于几何建模，不依赖大量标注数据，具备强泛化能力。采用抓取能力评分（GAS）评估性能，在15种物体和健康受试者上达到91%的SOTA分数，且对未见物体仍保持高重建成功率，显著优于学习型模型。
原文摘要 · Abstract (English)
Grasping is a fundamental skill for interacting with and manipulating objects in the environment. However, this ability can be challenging for individuals with hand impairments. Soft hand exoskeletons designed to assist grasping can enhance or restore essential hand functions, yet controlling these soft exoskeletons to support users effectively remains difficult due to the complexity of understanding the environment. This study presents a vision-based predictive control framework that leverages contextual awareness from depth perception to predict the grasping target and determine the next control state for activation. Unlike data-driven approaches that require extensive labelled datasets and struggle with generalizability, our method is grounded in geometric modelling, enabling robust adaptation across diverse grasping scenarios. The Grasping Ability Score (GAS) was used to evaluate performance, with our system achieving a state-of-the-art GAS of 91% across 15 objects and healthy participants, demonstrating its effectiveness across different object types. The proposed approach maintained reconstruction success for unseen objects, underscoring its enhanced generalizability compared to learning-based models.
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