Autonomous Tissue Manipulation via Surgical Robot Using Learning Based
Model Predictive Control
release_mwvrp2osxfdcfpjf7s3gkqqvve
by
Changyeob Shin, Peter Walker Ferguson, Sahba Aghajani Pedram, Ji Ma,
Erik P. Dutson, Jacob Rosen
2019
Abstract
Tissue manipulation is a frequently used fundamental subtask of any surgical
procedures, and in some cases it may require the involvement of a surgeon's
assistant. The complex dynamics of soft tissue as an unstructured environment
is one of the main challenges in any attempt to automate the manipulation of it
via a surgical robotic system. Two AI learning based model predictive control
algorithms using vision strategies are proposed and studied: (1) reinforcement
learning and (2) learning from demonstration. Comparison of the performance of
these AI algorithms in a simulation setting indicated that the learning from
demonstration algorithm can boost the learning policy by initializing the
predicted dynamics with given demonstrations. Furthermore, the learning from
demonstration algorithm is implemented on a Raven IV surgical robotic system
and successfully demonstrated feasibility of the proposed algorithm using an
experimental approach. This study is part of a profound vision in which the
role of a surgeon will be redefined as a pure decision maker whereas the vast
majority of the manipulation will be conducted autonomously by a surgical
robotic system. A supplementary video can be found at:
http://bionics.seas.ucla.edu/research/surgeryproject17.html
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