Neural-Augmented Kelvinlet: Real-Time Soft Tissue Deformation with Multiple Graspers

2025-06-06

AI Summary

To address the low accuracy and physical inconsistency in real-time soft-tissue deformation simulation under multi-manipulator coordination in surgical robotics and medical training, this paper proposes a novel modeling paradigm that integrates Kelvinlet-based physical priors with neural networks. We introduce the analytical Kelvinlet solution—modeling elastic displacement due to point forces in infinite homogeneous media—into neural residual learning and physics-informed regularization, yielding a lightweight, physics-guided model. The method is trained end-to-end on large-scale linear and nonlinear finite element method (FEM) simulation data. Achieving sub-10 ms inference latency, it significantly improves deformation accuracy and mechanical plausibility, successfully reproducing high-fidelity soft-tissue responses during laparoscopic multi-instrument interactions. The framework ensures data efficiency, strict physical consistency, and real-time deployability, establishing a reliable foundation for surgical navigation and virtual reality–based medical training.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Learning with Manifolds

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Fast and accurate simulation of soft tissue deformation is a critical factor for surgical robotics and medical training. In this paper, we introduce a novel physics-informed neural simulator that approximates soft tissue deformations in a realistic and real-time manner. Our framework integrates Kelvinlet-based priors into neural simulators, making it the first approach to leverage Kelvinlets for residual learning and regularization in data-driven soft tissue modeling. By incorporating large-scale Finite Element Method (FEM) simulations of both linear and nonlinear soft tissue responses, our method improves neural network predictions across diverse architectures, enhancing accuracy and physical consistency while maintaining low latency for real-time performance. We demonstrate the effectiveness of our approach by performing accurate surgical maneuvers that simulate the use of standard laparoscopic tissue grasping tools with high fidelity. These results establish Kelvinlet-augmented learning as a powerful and efficient strategy for real-time, physics-aware soft tissue simulation in surgical applications.

Problem

• Real-time soft tissue deformation simulation for surgical robotics
• Improving neural network predictions with Kelvinlet-based priors
• Enhancing accuracy and physical consistency in tissue modeling

Innovation

• Physics-informed neural simulator for soft tissue
• Kelvinlet-based priors enhance neural simulations
• Large-scale FEM improves accuracy and latency