Confidence-Based Intent Prediction for Teleoperation in Bimanual Robotic Suturing

Robotic-assisted procedures offer enhanced precision, but while fully autonomous systems are limited in task knowledge, difficulties in modeling unstructured environments, and generalization abilities, fully manual teleoperated systems also face challenges such as delay, stability, and reduced sensory information. To address these limitations, we propose an interactive control strategy that assists the human operator by predicting their motion plan at both high and low levels. At the high level, a surgeme recognition system is employed through a Transformer-based real-time gesture classification model to dynamically adapt to the operator's actions. At the low level, a Confidence-based Intention Assimilation Controller adjusts robot actions based on inferred user intent and shared control paradigms. The system is built around a robotic suturing task, supported by sensors that capture robot kinematics and task dynamics. Experimental results across users with varying skill levels demonstrate the effectiveness of the proposed approach, yielding statistically significant improvements in task completion time and user satisfaction compared with traditional teleoperation.