A State‐Adaptive Koopman Control Framework for Real‐Time Deformable Tool Manipulation in Robotic Environmental Swabbing

Deformable object manipulation (DOM) remains a critical challenge in robotics due to thecomplexities of developing suitable model‐based control strategies. Deformable tool manipulation (DTM) further complicates this task by introducing additional uncertainties between the robot and its environment. While humans effortlessly manipulate deformable tools using touch and experience, robotic systems struggle to maintain stability and precision. These challenges are particularly evident in environmental swab sampling for food safety, where a soft, sponge‐tipped swab must maintain prescribed normal force and uniform surface coverage. To address these challenges, we adapt a state‐adaptive Koopman‐based linear quadratic regulator (SA‐KLQR) control framework for real‐time deformable swabbing tool manipulation, demonstrated in environmental swab sampling for food safety. This method leverages Koopman operator‐based control to linearize nonlinear dynamics while adapting to state‐dependent variations in tool deformation and contact forces. A tactile‐based feedback system dynamically estimates and regulates the swab tool's angle, contact pressure, and surface coverage, ensuring compliance with food safety standards. Additionally, a sensor‐embedded contact pad monitors force distribution to mitigate tool pivoting and deformation, improving stability during dynamic interactions. Experimental results validate the SA‐KLQR approach, demonstrating accurate contact angle estimation, robust trajectory tracking, and reliable force regulation. The proposed framework enhances precision, adaptability, and real‐time control in deformable swabbing tool manipulation, adapting data‐driven learning with optimal control in a challenging robotic industrial swabbing interaction task.