Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots
This work addresses the challenge of reducing surgeon strain in minimally invasive surgery by enabling robots to learn complex tasks, though it is incremental as it builds on existing learning-from-demonstration methods.
The authors tackled the problem of automating context-dependent surgical tasks for tendon-driven robots by learning from expert demonstrations, achieving successful motion planning and execution for novel contexts on three surgery-inspired tasks.
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.