# Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability

arXiv: https://arxiv.org/abs/2607.13475 · HTML: https://mlsanity.com/paper/2607.13475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators](https://mlsanity.com/paper/2108.09432/) (2021)
- [Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation](https://mlsanity.com/paper/2503.11999/) (2025)
- [Learning from Demonstrations for Autonomous Soft-tissue Retraction](https://mlsanity.com/paper/2110.00336/) (2021)
- [Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement Learning](https://mlsanity.com/paper/2309.00837/) (2023)

## Abstract (arXiv)

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.