Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability
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. Res...
| Authors | Everest Yang, Skye Thompson, George D. Konidaris |
|---|---|
| Primary category | cs.RO |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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