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**Abstract:** Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful technique for measuring distances between biomolecules with nanometer precision. However, accurate distance determination is critically sensitive to variations in dye orientation, linker length, and environmental factors. This paper introduces a novel framework, "Iterative Perturbation and Machine Learning Calibration" (IPMLC), for automated and highly precise smFRET distance measurement. IPMLC leverages a machine learning (ML) model trained on simulated FRET efficiencies with varying parameters to iteratively perturb experimental data, refine dye orientation angles, and ultimately achieve sub-angstrom distance accuracy – a 10x improvement over traditional calibration methods.

**초록:** 본 연구는 인간과의 안전하고 효율적인 물리적 협업을 위한 로봇 제어 문제를 탐구한다. 구체적으로, 사람이 예측 불가능한 움직임을 보이는 환경에서 로봇이 안전 거리를 실시간으로 조절하는 방법을 제안한다. 기존의 강화학습 기반 접근 방식은 주로 현재 상태만을 고려하여 안전 거리를 결정하지만, 본 연구에서는 사람이 미래에 취할 가능성이 높은 행동을 예측하고, 이를 기반으로 안전 거리를 동적으로 조절하는 예측 기반 강화학습 (Predictive Reinforcement Learning, PRL) 프레임워크를 제시한다.

**Abstract:** This paper introduces a novel approach for characterizing dynamic electrochemical interfaces utilizing Dynamic Force Microscopy (DFM) combined with an adaptive force-distance mapping technique and machine learning hybridization. Current DFM methods struggle with accurately representing complex interfacial dynamics due to limitations in force feedback speed and interpretation. Our system overcomes these limitations by employing a Kalman filter-based adaptive mapping algorithm to predict the evolving tip-surface interaction potential in real-time, coupled with a recurrent neural network (RNN) trained on synthesized and experimental DFM data.