Affordance-Based Disambiguation of Surgical Instructions for Collaborative Robot-Assisted Surgery

Abstract

Effective human-robot collaboration in surgery is affected by the inherent ambiguity of verbal communication. This paper presents a framework for a robotic surgical assistant that interprets and disambiguates verbal instructions from a surgeon by grounding them in the visual context of the operating field. The system employs a two-level affordance-based reasoning process that first analyzes the surgical scene using a multimodal vision-language model and then reasons about the instruction using a knowledge base of tool capabilities. To ensure patient safety, a dual-set conformal prediction method is used to provide a statistically rigorous confidence measure for robot decisions, allowing it to identify and flag ambiguous commands. We evaluated our framework on a curated dataset of ambiguous surgical requests from cholecystectomy videos, demonstrating a general disambiguation rate of 60% and presenting a method for safer human-robot interaction in the operating room.