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

Arturas Straizys, Michael Burke, Subramanian Ramamoorthy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication2024 IEEE International Conference on Robotics and Automation (ICRA)
Number of pages7
Publication statusAccepted/In press - 29 Jan 2024
Event2024 IEEE International Conference on Robotics and Automation - Yokohama, Japan
Duration: 13 May 202417 May 2024


Conference2024 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2024
Internet address


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