Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots

TL;DR

This paper introduces a learning system for tendon-driven surgical robots that can perform context-dependent tasks by learning from expert demonstrations, enabling adaptable and automated surgical procedures.

Contribution

The work presents three models conditioned on context vectors that enable tendon-driven robots to generalize surgical tasks to new, unseen contexts from demonstrations.

Findings

Models successfully perform tasks in new contexts

Effective learning from limited expert demonstrations

Potential to automate complex surgical procedures

Abstract

Tendon-driven robots, a type of continuum robot, have the potential to reduce the invasiveness of surgery by enabling access to difficult-to-reach anatomical targets. In the future, the automation of surgical tasks for these robots may help reduce surgeon strain in the face of a rapidly growing population. However, directly encoding surgical tasks and their associated context for these robots is infeasible. In this work we take steps toward a system that is able to learn to successfully perform context-dependent surgical tasks by learning directly from a set of expert demonstrations. We present three models trained on the demonstrations conditioned on a vector encoding the context of the demonstration. We then use these models to plan and execute motions for the tendon-driven robot similar to the demonstrations for novel context not seen in the training set. We demonstrate the efficacy of our method on three surgery-inspired tasks.