RL◆ AI-generated · Sourced
Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability
TL;DR
arXiv:2607.13475v1 proposes a geometry-regularized learned state estimator that reconstructs the full deformable mesh state from only 40 noisy vertex observations, achieving 98.1% of oracle performance in multi-step retraction within a 2D deformable sheet simulation.
Problem Context
Surgical tissue retraction requires effective manipulation planning under partial and noisy perception. This work studies state estimation for deformable tissue retraction, where only sparse, noisy observations of the tissue surface are available at decision time.
Method Design
- A learned end-to-end state estimator is proposed, taking 40 noisy vertex coordinates as input and reconstructing the full deformable mesh state;
- Architecture combines a multilayer perceptron (MLP) with a low-dimensional PCA latent representation;
- Trained with geometry-aware regularization, explicitly enforcing smoothness and physical plausibility of predicted deformations.
Experimental Validation
Evaluated in a 2D deformable sheet simulation:
- Supports both single-step and multi-step retraction planning;
- Achieves 98.1% of oracle performance in multi-step retraction;
- Enables efficient inference, suitable for potential real-time surgical assistance.
Sources (compliance trail)
https://arxiv.org/abs/2607.13475