部分観測下での自律手術組織牽引のための変形可能状態推定
Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability
手術中の組織牽引において、まばらな観測から変形メッシュ全体を再構成する学習ベースの状態推定器を提案し、幾何学的正則化により滑らかで物理的に妥当な変形を実現した。
著者: Everest Yang, Skye Thompson, George D. Konidaris
分類: cs.RO, cs.LG
原文アブストラクト
Surgical tissue retraction requires effective manipulation planning under partial and noisy perception. We study state estimation for deformable tissue retraction, where only sparse observations of the tissue surface are available at decision time. We propose a learned state estimator that reconstructs the full deformable mesh state from 40 noisy vertex observations. The estimator combines a multilayer perceptron with a low-dimensional PCA latent representation and is trained using geometry-aware regularization that encourages smooth and physically plausible deformations. We evaluate the approach in a 2D deformable sheet simulation using single-step and multi-step retraction planning. Results show that the learned estimator achieves 98.1% of oracle performance in multi-step retraction while supporting efficient inference. These results demonstrate that learned, geometry-regularized state estimation can support effective deformable manipulation under realistic perception constraints.