用同一套软夹爪实现人操作采集与机器人执行，解决动作迁移中的感知与形态差异问题。
MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap
- 夹爪兼具手持采集和机器人末端功能，硬件一致避免域差距。
- 内置摄像头与软多面体网络，实时估算6自由度力和接触形变。
- 支持远程操控、离线学习和混合现实交互，适合机器人技能迁移研究者。
将人类示范动作迁移到机器人执行常受感知与形态差异导致的“域差距”阻碍。本文提出MagiClaw，一种双功能两指末端执行器，可作为手持工具进行直观数据采集，也可作为机器人末端执行器部署策略，确保硬件一致性与可靠性。每根手指集成嵌入式相机的软多面体网络（SPN），实现6-DoF力和接触形变的视觉估计。该系统融合来自集成iPhone的外感受环境感知——包括6D姿态、RGB视频及基于LiDAR的深度图。通过定制iOS应用，MagiClaw可实时传输同步的多模态数据，支持远程操作、离线策略学习以及混合现实界面下的沉浸式控制。实验表明，该统一架构显著降低了高保真、富含接触信息数据集的采集门槛，并加速了通用抓取策略的开发。更多详情请访问iOS应用：https://apps.apple.com/cn/app/magiclaw/id6661033548。
原文摘要 · Abstract (English)
The transfer of manipulation skills from human demonstration to robotic execution is often hindered by a "domain gap" in sensing and morphology. This paper introduces MagiClaw, a versatile two-finger end-effector designed to bridge this gap. MagiClaw functions interchangeably as both a handheld tool for intuitive data collection and a robotic end-effector for policy deployment, ensuring hardware consistency and reliability. Each finger incorporates a Soft Polyhedral Network (SPN) with an embedded camera, enabling vision-based estimation of 6-DoF forces and contact deformation. This proprioceptive data is fused with exteroceptive environmental sensing from an integrated iPhone, which provides 6D pose, RGB video, and LiDAR-based depth maps. Through a custom iOS application, MagiClaw streams synchronized, multi-modal data for real-time teleoperation, offline policy learning, and immersive control via mixed-reality interfaces. We demonstrate how this unified system architecture lowers the barrier to collecting high-fidelity, contact-rich datasets and accelerates the development of generalizable manipulation policies. Please refer to the iOS app at https://apps.apple.com/cn/app/magiclaw/id6661033548 for further details.
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