Bayesian-Optimized Impedance Control of an Aerial Robot for Safe Physical Interaction with the Environment

Impedance control is a widely used interaction-control technique for aerial and ground robots. To achieve consistent performance during impedance control tasks, an a-priori knowledge of the environment parameters is needed to adjust the controller's impedance parameters accordingly. Concentrating on tasks requiring constant impedance parameters throughout operation, a model-free learning framework is proposed to autonomously find the suitable parameters values. The framework relies on Bayesian optimization and episodic reward calculation requiring the drone to repeatedly perform a predetermined task in the environment actively searching in the impedance parameters space. The sample-efficiency and safety of learning were improved by adding two novel modifications to standard Bayesian optimization. The proposed technique was validated in a high fidelity simulation environment. The results show that the proposed framework is able to automatically find suitable impedance parameters values in different situations given the same initial knowledge and that the learned parameters values can be generalized to similar interaction tasks.