手術ロボットトランスフォーマー（SRT）：手術タスクのための模倣学習
Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks
da Vinci手術ロボットの不正確な順運動学データでも模倣学習を成功させる相対行動定式化を提案し、組織操作・針操作・結紮の3タスクで実証した。
著者: Ji Woong Kim, Tony Z. Zhao, Samuel Schmidgall, Anton Deguet, Marin Kobilarov, Chelsea Finn, Axel Krieger
分類: cs.RO
原文アブストラクト
We explore whether surgical manipulation tasks can be learned on the da Vinci robot via imitation learning. However, the da Vinci system presents unique challenges which hinder straight-forward implementation of imitation learning. Notably, its forward kinematics is inconsistent due to imprecise joint measurements, and naively training a policy using such approximate kinematics data often leads to task failure. To overcome this limitation, we introduce a relative action formulation which enables successful policy training and deployment using its approximate kinematics data. A promising outcome of this approach is that the large repository of clinical data, which contains approximate kinematics, may be directly utilized for robot learning without further corrections. We demonstrate our findings through successful execution of three fundamental surgical tasks, including tissue manipulation, needle handling, and knot-tying.
