用40个点观测重建可变形组织状态，提升手术器械操控精度
Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability
- 基于PCA降维与MLP的神经网络重构完整组织形变
- 多步操作下达到98.1%的基准性能，支持快速推理
- 适合在感知不全、噪声大的手术场景中使用
手术组织牵拉需在部分且嘈杂的感知条件下进行有效操作规划。本文研究可变形组织牵拉中的状态估计问题，仅在决策时刻获得组织表面稀疏观测。提出一种学习型状态估计算法，从40个噪声顶点观测中重建完整的可变形网格状态。该方法结合多层感知机与低维PCA隐空间表示，并采用几何感知正则化训练，鼓励平滑且物理合理的形变。在二维可变形薄片模拟中评估单步与多步牵拉规划，结果表明该方法在多步牵拉中达到98.1%的基准性能，同时支持高效推理。结果证明，学习型、几何正则化状态估计可在真实感知约束下支持有效的可变形操作。
原文摘要 · Abstract (English)
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.
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