Source note
OpenRC: An Open-Source Robotic Colonoscopy Framework for Multimodal Data Acquisition and Autonomy Research
Summary
OpenRC is an open-source robotic colonoscopy platform that retrofits standard scopes and records synchronized video, operator actions, robot state, and distal tip pose. The paper’s main contribution is the combination of low-cost hardware validation and a multimodal dataset for closed-loop autonomy research in colonoscopy.
Problem
- Colonoscopy research often splits perception and control: many datasets have video only, while robotic systems often lack shared, synchronized logs for actions, actuation, and tip motion.
- This blocks reproducible work on closed-loop robotic colonoscopy, control-aware perception, and vision-language-action training, where observations, actions, and state must be aligned.
- The problem matters because colonoscopy is central to colorectal cancer screening, yet adenoma miss rates can reach 34%, and operator variability and device limits still affect outcomes.
Approach
- The authors build a modular robotic retrofit for conventional colonoscopes with 3 clinically relevant DoFs: insertion/retraction and two distal bending axes.
- The system logs four main modalities on a shared ROS 2 stack: colonoscope video, operator command vectors, motor/actuation state, and 6-DoF EM-tracked distal tip pose.
- They validate timing and motion consistency with controlled sinusoidal excitation, then estimate modality offsets and resample all streams to 30 Hz using video as the reference.
- They collect teleoperated data in two colon phantoms and store the dataset in LeRobot 2.1 format, including task instructions and episodes for navigation, failures, and recovery.
Results
- The full framework, excluding the EM tracker, can be assembled for under $5,000 USD.
- The dataset contains 1,894 episodes and about 19 hours of teleoperated colonoscopy data across 10 task variations.
- The dataset includes 142 failure episodes and 141 recovery episodes, covering cases such as lumen loss, wall contact, and fold engagement.
- In timing characterization, estimated offsets relative to control actions are about 102 ms for motor encoder state, 435 ms for EM tracking, and 412 ms for optical-flow-derived motion.
- After alignment, the median residual lag between operator action and actuation state is 55.6 ms (about 1.6 frames at 30 Hz).
- After alignment, the residual lag between actuation state and distal tip pose is centered at 0.0 ms. The paper does not report task-performance gains against an autonomy baseline; its strongest concrete claim is that it provides an open, synchronized platform and dataset that prior colonoscopy resources did not combine.