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About

Johanna Bethge is a robotics and control systems researcher whose work sits at the intersection of model predictive control (MPC), force and motion control, and machine learning-supported robotics. Her research addresses one of the central challenges in modern robotics: enabling robotic manipulators to follow precise paths while accurately managing contact forces, even under conditions of uncertainty and imprecise environmental knowledge. Her most influential contributions include pioneering frameworks for predictive path following combined with admittance and force control, demonstrated on lightweight robots, which together have garnered over 55 citations. These works provide practical solutions for manipulation tasks where surface geometry, stiffness, or positioning is not fully known in advance. Bethge has also advanced the integration of machine learning into MPC frameworks, developing multi-mode learning-supported controllers capable of adapting to varying operating conditions — such as robotic grasping of objects with unknown properties — while maintaining formal safety guarantees. Her 2023 work on safe machine learning-supported predictive force control reflects a growing emphasis on certified, high-performance human-robot interaction and delicate object handling. Through this body of work, Bethge has established herself as a thoughtful contributor to safe, adaptive, and practically deployable robotic control systems.