RAPID Hand：面向通用机器人自主的鲁棒、经济、感知集成的灵巧操作平台
RAPID Hand: A Robust, Affordable, Perception-Integrated, Dexterous Manipulation Platform for Generalist Robot Autonomy
AI总结：
提出软硬件协同优化的 RAPID Hand 灵巧操作平台，集成 20-DoF 手、低延迟多模态感知与高-DoF 遥操作，实现低成本、高质量的真实多指操作数据采集。
摘要：
本文针对面向通用机器人自主、用于采集真实世界多指机器人操作数据的低成本且高灵巧性平台稀缺这一问题展开研究。为实现该目标，我们提出了 RAPID Hand，这是一个软硬件协同优化的平台，其中紧凑型 20-DoF 灵巧手、鲁棒的全手感知以及高-DoF 遥操作接口经过联合设计。具体而言，RAPID Hand 采用紧凑实用的手部本体，并采用硬件级感知框架，以低于 7 ms 的延迟和空间对齐能力稳定集成腕部视觉、指尖触觉传感和本体感知。在高-DoF 手上采集高质量演示具有挑战性，因为现有遥操作方法在复杂多指系统上难以兼顾精度与稳定性。我们通过通用驱动方案、定制感知电子器件以及两个重定向约束，对手部设计、感知集成和遥操作接口进行协同优化，从而解决该问题。我们评估了该平台的硬件、感知和遥操作接口。在采集的数据上训练 diffusion policy（扩散策略）表明，其性能优于先前工作，验证了该系统具备可靠、高质量数据采集的能力。该平台由低成本、现成可用的组件构建，并将公开发布，以确保可复现性和易于采用。
英文摘要：
This paper addresses the scarcity of low-cost but high-dexterity platforms for collecting real-world multi-fingered robot manipulation data towards generalist robot autonomy. To achieve it, we propose the RAPID Hand, a co-optimized hardware and software platform where the compact 20-DoF hand, robust whole-hand perception, and high-DoF teleoperation interface are jointly designed. Specifically, RAPID Hand adopts a compact and practical hand ontology and a hardware-level perception framework that stably integrates wrist-mounted vision, fingertip tactile sensing, and proprioception with sub-7 ms latency and spatial alignment. Collecting high-quality demonstrations on high-DoF hands is challenging, as existing teleoperation methods struggle with precision and stability on complex multi-fingered systems. We address this by co-optimizing hand design, perception integration, and teleoperation interface through a universal actuation scheme, custom perception electronics, and two retargeting constraints. We evaluate the platform's hardware, perception, and teleoperation interface. Training a diffusion policy on collected data shows superior performance over prior works, validating the system's capability for reliable, high-quality data collection. The platform is constructed from low-cost and off-the-shelf components and will be made public to ensure reproducibility and ease of adoption.
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。
