Interview with Nina Wiedemann and Valentin Wüest: developing a gradient-based control method for robotic systems

Nina Wiedemann, Valentin Wüest et al present an efficient and accurate control policy that is trained with the Analytic Policy Gradient method, and experiment with complex aerial robots such as a quadrotor (left). Their controller can track a reference trajectory accurately (right), within a fraction of the runtime required by online-optimisation methods such as MPC (bottom left). In their recent paper, Training Efficient Controllers via Analytic Policy Gradient, Nina Wiedemann, Valentin Wüest, Antonio Loquercio, Matthias Müller, Dario Floreano, and Davide Scaramuzza propose a gradient-based method for control of robotic systems. First authors Nina Wiedemann and Valentin Wüest told us more about their approach, the motivation for the work, and what they are planning next. Our paper is about control for robotic systems, with a focus on aerial vehicles.