MultiClear：用于透明物体抓取辅助的多模态软体外骨骼手套
MultiClear: Multimodal Soft Exoskeleton Glove for Transparent Object Grasping Assistance
AI总结：
本文提出多模态框架MultiClear，通过融合RGB、深度和听觉信号，结合分层控制架构与零样本分割视觉基础模型，提升了可穿戴软体外骨骼手套对透明物体的抓取辅助能力。
摘要：
抓取是与环境交互的一项基本技能。然而，对于某些人（例如由于残疾）来说，这种能力可能很困难。可穿戴机器人解决方案可以增强或恢复手部功能，最近的进展利用计算机视觉来提高抓取能力。然而，由于透明物体的视觉对比度差和深度线索模糊，抓取透明物体仍然具有挑战性。此外，虽然已经探索了结合触觉和听觉反馈的多模态控制策略来抓取透明物体，但视觉与这些模态的整合仍然不完善。本文介绍了MultiClear，这是一个多模态框架，旨在通过融合RGB数据、深度数据和听觉信号，增强可穿戴软体外骨骼手套对透明物体的抓取辅助。该外骨骼手套将腱驱动执行器与RGB-D相机和内置麦克风集成在一起。为了实现精确和自适应控制，提出了一种分层控制架构。对于所提出的分层控制架构，高层控制层提供上下文感知，中层控制层处理多模态感觉输入，低层控制执行PID电机控制以进行微调抓取调整。透明物体分割的挑战通过引入视觉基础模型进行零样本分割来应对。所提出的系统实现了70.37%的抓取能力得分，证明了其在透明物体操作中的有效性。
英文摘要：
Grasping is a fundamental skill for interacting with the environment. However, this ability can be difficult for some (e.g. due to disability). Wearable robotic solutions can enhance or restore hand function, and recent advances have leveraged computer vision to improve grasping capabilities. However, grasping transparent objects remains challenging due to their poor visual contrast and ambiguous depth cues. Furthermore, while multimodal control strategies incorporating tactile and auditory feedback have been explored to grasp transparent objects, the integration of vision with these modalities remains underdeveloped. This paper introduces MultiClear, a multimodal framework designed to enhance grasping assistance in a wearable soft exoskeleton glove for transparent objects by fusing RGB data, depth data, and auditory signals. The exoskeleton glove integrates a tendon-driven actuator with an RGB-D camera and a built-in microphone. To achieve precise and adaptive control, a hierarchical control architecture is proposed. For the proposed hierarchical control architecture, a high-level control layer provides contextual awareness, a mid-level control layer processes multimodal sensory inputs, and a low-level control executes PID motor control for fine-tuned grasping adjustments. The challenge of transparent object segmentation was managed by introducing a vision foundation model for zero-shot segmentation. The proposed system achieves a Grasping Ability Score of 70.37%, demonstrating its effectiveness in transparent object manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
