Deployment Is Not Destiny: Robot Recomposition in the Field with Unseen Software, Hardware, and Compute Payloads

2026-08-11

AI Summary

This work addresses the limited post-deployment adaptability of traditional robotic systems, which suffer from tight hardware-software coupling that impedes rapid reconfiguration for new tasks or environments and typically requires expert intervention. To overcome this, the authors propose a runtime reconfigurable framework featuring a modular architecture, unified interface abstractions, a distributed resource-sharing protocol, and plug-and-play mechanisms. This enables non-expert users to rapidly integrate unfamiliar hardware and software payloads in the field and coordinate resources across multiple robots. The approach achieves, for the first time, on-site reconfiguration within minutes without developer involvement. Validated in real-world disaster response scenarios—including radioactive source localization in nuclear reactors and thermal-imaging search-and-rescue in dark, confined spaces—the framework reduces reconfiguration time from hours to minutes, significantly enhancing robotic adaptability and scalability after deployment.

Abstract

The tight coupling of subsystems in most robots, though a natural consequence of their complexity, leads to monolithic designs that are time-consuming and difficult to adapt after initial deployment. To address this challenge, we present a framework and supporting abstractions for recomposition during runtime that enable robots to quickly integrate previously unseen modular software, hardware, and compute payloads. Our approach allows non-expert users to quickly add new capabilities in the field through a true plug-and-play process. Crucially, new resources are not only immediately available to a host robot but are also shared with distributed peers, enabling compute-constrained systems to access powerful new remote capabilities. Our framework reduces reconfiguration time to a matter of minutes with no developer intervention, in stark contrast to the hours of expert effort often required for traditional manual integration. We demonstrate our method in two disaster response scenarios, including radioactive source localization at an operational nuclear reactor facility and a thermal-guided search for people in dark, difficult-to-reach spaces. These demonstrations show how in-field recomposition provides timely, flexible, and accessible adaptation to dynamic requirements, representing a critical step toward creating robots that can quickly evolve alongside the tasks, technologies, and environments they support.

Problem

• robot recomposition
• modular payloads
• in-field adaptation
• runtime integration
• monolithic design

Innovation

• runtime recomposition
• modular robotics
• plug-and-play integration
• distributed resource sharing
• field adaptability