Deformable Object Manipulation (DOM) remains a critical challenge in robotics due to the complexities 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. To address these challenges, we present a novel State-Adaptive Koopman LQR (SA-KLQR) control framework for real-time deformable tool manipulation, demonstrated through a case study 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 tool manipulation, bridging the gap between data-driven learning and optimal control in robotic interaction tasks.

翻译：可变形物体操作（DOM）由于开发合适的基于模型控制策略的复杂性，在机器人学中仍是一个关键挑战。可变形工具操作（DTM）通过引入机器人与环境之间额外的不确定性，进一步复杂化了这一任务。虽然人类能够轻松地利用触觉和经验操作可变形工具，但机器人系统难以保持稳定性和精度。为应对这些挑战，我们提出了一种新颖的状态自适应库普曼线性二次调节器（SA-KLQR）控制框架，用于实时可变形工具操作，并通过食品安全领域的环境拭子采样案例研究进行验证。该方法利用基于库普曼算子的控制来线性化非线性动力学，同时适应工具变形和接触力的状态依赖性变化。一个基于触觉的反馈系统动态估计并调节拭子工具的角度、接触压力和表面覆盖度，确保符合食品安全标准。此外，一个嵌入式传感器的接触垫监测力分布，以减轻工具枢转和变形，提高动态交互过程中的稳定性。实验结果验证了SA-KLQR方法的有效性，展示了精确的接触角估计、鲁棒的轨迹跟踪和可靠的力调节。所提出的框架提升了可变形工具操作的精度、适应性和实时控制能力，弥合了机器人交互任务中数据驱动学习与最优控制之间的差距。