Shared Haptic Control for Surgical Skill Transfer on a Dual-Console Da Vinci Research Kit

Abstract

Robotic surgery has revolutionized minimally invasive procedures by offering enhanced precision, dexterity, and patient outcomes. However, the training and operational paradigms in robotic surgery have not evolved in parallel. Current apprenticeship models fall short in this domain, as robotic surgery isolates the primary surgeon in a teleoperated control loop, limiting opportunities for hands-on learning by trainees. To address this, we present the first implementation of a multilateral controller on a da Vinci Research Kit (dVRK), enabled by a four-channel teleoperation architecture and learning-based force estimation on a dual-console setup. This framework allows an expert and novice to share motion and force authority on the patient side robots through an adjustable dominance factor. We validated the system in three experiments. In transparency tests, the architecture achieved sub-millimeter position tracking errors (PTE <= 0.2mm) and force tracking errors (FTE) <= 1N. In a palpation pilot user study (N=10) with tumor-tissue phantoms, participants identified stiffer regions, without visual feedback, with 83% accuracy in single-user mode (alpha = 1) and 74% accuracy in dual-user shared mode (alpha = 0.5). In a suturing force control pilot user study (N=10), novices significantly reduced force error and increased time within the safe range after expert-guided training, with no suture breakage observed post-training. These results on a dual-console dVRK setup demonstrate the feasibility of expert-in-the-loop training with real-time haptic guidance, positioning multilateral teleoperation as a promising approach for surgical skill transfer.