Developing robotic manipulation policies is iterative and hypothesis-driven: researchers test tactile sensing, gripper geometries, and sensor placements through real-world data collection and training. Yet even minor end-effector changes often require mechanical refitting and system re-integration, slowing iteration. We present RAPID, a full-stack reconfigurable platform designed to reduce this friction. RAPID is built around a tool-free, modular hardware architecture that unifies handheld data collection and robot deployment, and a matching software stack that maintains real-time awareness of the underlying hardware configuration through a driver-level Physical Mask derived from USB events. This modular hardware architecture reduces reconfiguration to seconds and makes systematic multi-modal ablation studies practical, allowing researchers to sweep diverse gripper and sensing configurations without repeated system bring-up. The Physical Mask exposes modality presence as an explicit runtime signal, enabling auto-configuration and graceful degradation under sensor hot-plug events, so policies can continue executing when sensors are physically added or removed. System-centric experiments show that RAPID reduces the setup time for multi-modal configurations by two orders of magnitude compared to traditional workflows and preserves policy execution under runtime sensor hot-unplug events. The hardware designs, drivers, and software stack are open-sourced at https://rapid-kit.github.io/ .

翻译：开发机器人操作策略是一个迭代且基于假设的过程：研究人员通过真实世界数据收集和训练来测试触觉传感、夹持器几何形状及传感器布局。然而，即使是末端执行器的微小改动也常需机械重装与系统重新集成，从而拖慢迭代速度。本文提出RAPID，一个旨在减少此类摩擦的全栈可重构平台。RAPID围绕免工具、模块化的硬件架构构建，该架构统一了手持式数据采集与机器人部署；同时配备相应的软件栈，通过源自USB事件的驱动级物理掩码，实时感知底层硬件配置。这种模块化硬件架构将重构时间缩短至秒级，使系统性多模态消融研究切实可行，允许研究人员在无需重复系统启动的情况下，全面探索多种夹持器与传感配置。物理掩码将模态存在性作为显式的运行时信号，支持传感器热插拔事件下的自动配置与优雅降级，从而在传感器被物理增删时策略仍可持续执行。以系统为中心的实验表明，与传统工作流相比，RAPID将多模态配置的搭建时间缩短了两个数量级，并在运行时传感器热拔插事件下保持策略执行。硬件设计、驱动程序及软件栈已在 https://rapid-kit.github.io/ 开源。