  The prospect of using autonomous robots to enhance the capabilities of
physicians and enable novel procedures has led to considerable efforts in
developing medical robots and incorporating autonomous capabilities. Motion
planning is a core component for any such system working in an environment that
demands near perfect levels of safety, reliability, and precision. Despite the
extensive and promising work that has gone into developing motion planners for
medical robots, a standardized and clinically-meaningful way to compare
existing algorithms and evaluate novel planners and robots is not well
established. We present the Medical Motion Planning Dataset (Med-MPD), a
publicly-available dataset of real clinical scenarios in various organs for the
purpose of evaluating motion planners for minimally-invasive medical robots.
Our goal is that this dataset serve as a first step towards creating a larger
robust medical motion planning benchmark framework, advance research into
medical motion planners, and lift some of the burden of generating medical
evaluation data.
