发表机构
Brown University（布朗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究部分可观测下自主手术组织牵开的可变形状态估计问题，提出结合多层感知器与低维PCA潜在表示、用几何感知正则化训练的状态估计器，在二维模拟中评估，结果显示该估计器在多步牵开中性能良好，能支持有效可变形操作。
AI 中文摘要
手术组织牵开需要在部分且有噪声的感知下进行有效的操作规划。我们研究可变形组织牵开的状态估计，在决策时只有组织表面的稀疏观测可用。我们提出一种学习状态估计器，它从40个有噪声的顶点观测重建完整的可变形网格状态。该估计器将多层感知器与低维PCA潜在表示相结合，并使用几何感知正则化进行训练，以鼓励平滑且符合物理原理的变形。我们在二维可变形片模拟中使用单步和多步牵开规划评估该方法。结果表明，学习到的估计器在多步牵开中达到了98.1%的神谕性能，同时支持高效推理。这些结果表明，学习到的、几何正则化的状态估计可以在现实感知约束下支持有效的可变形操作。
英文摘要
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.
CommentsAccepted to the ICRA 2026 RASEI Workshop
