RAPID: Reconfigurable, Adaptive Platform for Iterative Design

TL;DR

RAPID is a reconfigurable robotic platform with modular hardware and software that significantly accelerates iterative manipulation policy development by enabling quick reconfiguration and robust runtime sensor management.

Contribution

It introduces a full-stack, hardware-software reconfigurable platform that simplifies and speeds up the process of testing various robotic end-effector configurations.

Findings

Reduces reconfiguration time to seconds

Enables systematic multi-modal ablation studies

Maintains policy execution during sensor hot-plug events

Abstract

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/ .