Generating robotic elliptical excisions with human-like tool-tissue interactions

TL;DR

This paper presents a robotic learning framework for elliptical surgical excisions that models human-like tool-tissue interactions, enabling analysis and optimization of surgical techniques to improve safety and efficacy.

Contribution

It introduces a parameterised skill model and Bayesian optimization to learn and refine robotic surgical behaviors from limited demonstrations.

Findings

Robots can replicate human-like excision forces.

Optimized robot behavior aligns with expert ratings.

Framework enables large-scale analysis of surgical techniques.

Abstract

In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.