Prediction from expert demonstrations for safe tele-surgery

Abstract

Abstract:In this paper, we propose a new prediction from expert demonstration (PED) methodology to improve reliability and safety in tele-surgery. Data was collected from expert (clinician) demonstrations for the procedure of trocar insertion. We encoded a set of force, torque and penetration trajectories by using a Gaussian mixture model (GMM). A generalization of these profiles and associated parameters were retrieved by Gaussian mixture regression (GMR). We validated the proposed methodology for tele-robotic placement of the trocar in two stages. First, we tested the efficacy of the proposed PED approach for handling transmission error and latency. Our results showed that for the average case (12% packet error and 10% loss of packet), a 58.8% improvement in performance was obtained in comparison to using an extended Kalman filter. Next, we validated the methodology for surgical assistance on 15 participants. A haptic assistance mode was devised based on the proposed PED model to assist inexperienced operators to perform the procedure. The PED model was tested for instrument deviation, penetration force and penetration depth. Preliminary study results showed that participants with PED assistance performed the task with more consistency and exerted lesser penetration force than subjects without assistance.

Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation

“…Increased precision and safety, as well as a reduction in task completion time, is the promise of this control method. It has been applied to many different fields, such as remote assembly [7], telesurgery [8], vehicle control [9], and even space-to-ground telemanipulation with long time-delay [10]. To provide effective guidance feedback, reliable and accurate task position information is required, along with the trajectories to guide the operator.…”

“…In the field of haptically guided telemanipulation, LfD has been recently used to address different tasks related to surgical robotics. Chowriappa et al [8] used LfD to optimize the trocar placement in minimally invasive surgery (MIS). They collected a set of forces, torques, and trajectories from multiple demonstrations of the task and encoded them through the LfD approach.…”

A predictive model for haptic assistance in robot assisted trocar insertion

“…This paper describes a methodology to characterize surgical skill to provide haptic assistance; in order to improve safety in robot assisted surgical procedures. We extend on our prior work, on modeling of trocar insertion [4,5] and sharing of haptics sensations using tele-haptics [6,7], to address the problems of robot assisted trocar insertion.…”

Guiado háptico mediante aprendizaje por demostración en robótica quirúrgica

“…Aprendizaje por Demostración (Learning for Demostration, LfD) para la generación de referencias de guiado háptico que han sido previamente entrenadas por usuarios expertos. Actualmente, este enfoque se ha usado de manera amplia en robótica, para la generación de trayectorias continuas basadas en la posición del manipulador [5], [11], o medidas de fuerzas de contacto [8], [14]. Sin embargo, aún no se ha logrado la aplicación directa de guiado háptico basado en LfD en un sistema de teleoperación quirúrgica.…”