Few-Human-Interaction Reinforcement Learning for Autonomous Transbronchial Intervention

The transbronchial interventional surgery presents challenges with winding and convoluted pathways, prone to compression and friction. Current autonomous planning struggles to reach deeper bronchial positions, and hard to consider multiple conflicting goals simultaneously. This article introduces an innovative planning scheme with preference weights to achieve smooth, frictionless, and collision-free autonomous transbronchial intervention with continuum robot (CR). A few-human-interaction twin-delayed deep deterministic policy gradient (FHITD3) generated from surgeon preference guidance is proposed, which determines the optimal strategy for the motion of CR. Preference knowledge is generated through interaction between human and few diversity samples. An abstract actuator space description is proposed for the posture and position representation of CR during movement within bronchus. A contact motion analysis strategy is proposed to calculate real-time attitude of CR in contact with bronchus. In addition, an oscillation suppression approach to address CR's unsmooth distal end trajectory is proposed. Simulated experiments show that the CR autonomously completes intervention tasks with a smooth and stable trajectory, reducing distal end oscillation by over 45%. It achieves a target endpoint within the fourth level bronchus (approximately 5 mm diameter) with over 90% probability.