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[2602.15834] A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation
Summary
This paper presents a Koopman-Bayesian framework to enhance haptic surgical simulations, improving realism through nonlinear dynamics and perceptual psychophysics.
Why It Matters
The integration of high-fidelity haptic feedback in surgical simulations is crucial for training medical professionals. This framework not only enhances realism but also personalizes force feedback based on individual perceptual thresholds, potentially revolutionizing surgical education and practice.
Key Takeaways
- Introduces a novel Koopman-Bayesian framework for surgical simulations.
- Achieves significant improvements in force rendering latency and perceptual discrimination.
- Utilizes Bayesian calibration to tailor feedback to individual users' perceptual limits.
- Outperforms traditional rendering methods in various surgical tasks.
- Highlights future potential for closed-loop neural feedback in haptic interfaces.
Computer Science > Machine Learning arXiv:2602.15834 (cs) [Submitted on 4 Jan 2026] Title:A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation Authors:Rohit Kaushik, Eva Kaushik View a PDF of the paper titled A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation, by Rohit Kaushik and Eva Kaushik View PDF HTML (experimental) Abstract:We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an augmented state space with a Koopman operator formulation, allowing linear prediction and control of the dynamics that are nonlinear by nature. To make the rendered forces consistent with human perceptual limits, we put forward a Bayesian calibration module based on WeberFechner and Stevens scaling laws, which progressively shape force signals relative to each individual's discrimination thresholds. For various simulated surgical tasks such as palpation, incision, and bone milling, the proposed system attains an average rendering latency of 4.3 ms, a force error of less than 2.8% and a 20% improvement in perceptual discrimination. Multivariate statistical analyses (MANOVA and regression) reveal that the system's performance is significantly better than that of conventional spring-damper and energy, based rendering me...
