Papers

ColoACT: Multi-Cue Action Chunking for Smooth Autonomous Colon Navigation on a Self-Propelled Endoscopic Robot

This is a preprint describing ColoACT, an imitation-learning navigation system for a compact self-propelled endoscopic robot. The policy fuses RGB, relative depth, and a gradient-based pseudo-elevation map with an Action Chunking Transformer and temporal ensembling. In ex-vivo porcine colons it reached 85.4% success on straight and 72.5% on curved segments, with smooth steering and success on 90-degree turns (70%) and double bends (60%), suggesting a path toward reducing physician burden in colonoscopy.