OpenRC: An Open-Source Robotic Colonoscopy Framework for Multimodal Data Acquisition and Autonomy Research

TL;DR

OpenRC is an open-source robotic colonoscopy platform that enables systematic multimodal data collection and research in surgical autonomy, supporting reproducible experiments and advancing vision-language-action learning.

Contribution

It introduces a modular, open hardware framework for robotic colonoscopy that records multimodal data and facilitates reproducible research in surgical autonomy.

Findings

Validated motion consistency and measured latency across sensing streams.

Collected a comprehensive dataset of 1,894 teleoperated episodes over 19 hours.

Supported various navigation, failure, and recovery tasks for research.

Abstract

Colorectal cancer screening critically depends on colonoscopy, yet existing platforms offer limited support for systematically studying the coupled dynamics of operator control, instrument motion, and visual feedback. This gap restricts reproducible closed-loop research in robotic colonoscopy, medical imaging, and emerging vision-language-action (VLA) learning paradigms. To address this challenge, we present OpenRC, an open-source modular robotic colonoscopy framework that retrofits conventional scopes while preserving clinical workflow. The framework supports simultaneous recording of video, operator commands, actuation state, and distal tip pose. We experimentally validated motion consistency and quantified cross-modal latency across sensing streams. Using this platform, we collected a multimodal dataset comprising 1,894 teleoperated episodes ~19 hours across 10 structured task variations of routine navigation, failure events, and recovery behaviors. By unifying open hardware and an aligned multimodal dataset, OpenRC provides a reproducible foundation for research in multimodal robotic colonoscopy and surgical autonomy.