RAPID: Reconfigurable, Adaptive Platform for Iterative Design

2026-02-06

AI Summary

This work addresses the inefficiency in robotic manipulation strategy development caused by minor end-effector modifications, which typically necessitate cumbersome mechanical reassembly and system integration. To overcome this bottleneck, the authors propose a hardware-software co-designed reconfigurable platform featuring tool-free modular mechanical design, a USB event-driven hardware identification mechanism, and an innovative “Physical Mask” that encodes sensor modality presence as a runtime signal. This enables automatic perception configuration adaptation and graceful degradation during hot-swapping. The platform supports sub-second multimodal configuration switching without interrupting policy execution, reducing reconfiguration time by two orders of magnitude compared to conventional workflows. All associated hardware and software components have been open-sourced.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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/ .

Problem

• robotic manipulation
• iterative design
• hardware reconfiguration
• sensor integration
• end-effector

Innovation

• reconfigurable hardware
• modular robotics
• Physical Mask
• sensor hot-plug
• rapid prototyping