Physics-Informed Machine Learning for Efficient Sim-to-Real Data Augmentation in Micro-Object Pose Estimation

2025-11-20

AI Summary

Optical microrobot pose estimation suffers from scarcity of real microscopic image data, high annotation costs, and limitations of existing digital twins in modeling complex optical phenomena—such as diffraction artifacts and depth-of-field variations. Method: This paper proposes a physics-informed deep generative framework that integrates wave-optics rendering into a generative adversarial network (GAN), coupled with a depth-alignment mechanism and a CNN-based pose estimator, enabling high-fidelity, generalizable synthetic microscopy image generation. Contribution/Results: The method supports unseen-pose extrapolation and zero-shot data augmentation for novel microrobot geometries. Experiments show a 35.6% SSIM improvement over pure AI baselines and a generation speed of 0.022 s/frame. A pose estimator trained solely on synthetic data achieves 93.9% and 91.9% accuracy in pitch and roll angle estimation—nearly matching performance attained with real-data training.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Precise pose estimation of optical microrobots is essential for enabling high-precision object tracking and autonomous biological studies. However, current methods rely heavily on large, high-quality microscope image datasets, which are difficult and costly to acquire due to the complexity of microrobot fabrication and the labour-intensive labelling. Digital twin systems offer a promising path for sim-to-real data augmentation, yet existing techniques struggle to replicate complex optical microscopy phenomena, such as diffraction artifacts and depth-dependent imaging.This work proposes a novel physics-informed deep generative learning framework that, for the first time, integrates wave optics-based physical rendering and depth alignment into a generative adversarial network (GAN), to synthesise high-fidelity microscope images for microrobot pose estimation efficiently. Our method improves the structural similarity index (SSIM) by 35.6% compared to purely AI-driven methods, while maintaining real-time rendering speeds (0.022 s/frame).The pose estimator (CNN backbone) trained on our synthetic data achieves 93.9%/91.9% (pitch/roll) accuracy, just 5.0%/5.4% (pitch/roll) below that of an estimator trained exclusively on real data. Furthermore, our framework generalises to unseen poses, enabling data augmentation and robust pose estimation for novel microrobot configurations without additional training data.

Problem

• Addresses costly microrobot pose estimation data acquisition challenges
• Overcomes limitations in simulating optical microscopy artifacts digitally
• Enables robust pose estimation for unseen microrobot configurations

Innovation

• Integrates wave optics physics into generative adversarial network
• Synthesizes high-fidelity microscope images for microrobot pose estimation
• Achieves real-time rendering while improving structural similarity significantly