**Abstract:** This research presents a novel framework for enhancing human-robot interaction (HRI) in collaborative assembly tasks by leveraging a multi-modal contextual reasoning engine and an adaptive gesture recognition system. Existing HRI solutions often struggle with ambiguous gestures and lack robust contextual understanding, leading to inefficiencies and frustration. Our system, HyperScore-Adaptive Assembly Robotics (HSAAR), addresses these limitations by integrating a hierarchical Bayesian network for probabilistic contextual modeling with a deep learning-based gesture recognition module capable of real-time adaptation to individual user styles.

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**Abstract:** This paper introduces a novel approach to human-robot interaction (HRI) focused on enhancing intuitive control and task execution through predictive kinesthetic feedback (PKF) modeling in collaborative robotics. Traditional HRI relies on reactive feedback, often leading to perceived stiffness and unnatural movements. We propose a framework that utilizes a multi-layered evaluation pipeline to preemptively model and compensate for robot kinematic uncertainties, enabling a more fluid and perceptually kinesthetic interaction.