A Reinforcement Learning-Based Control Strategy for Robust Interaction of Robotic Systems with Uncertain Environments

In the context of interaction with unmodelled systems, it becomes imperative for a robot controller to possess the capability to dynamically adjust its actions in real-time, enhancing its resilience in the face of fluctuating environmental conditions. This adaptation process must be performed in a stability-preserving fashion, and resourcefully exploit the knowledge acquired during the interaction process. In this article, we propose a novel control strategy, based on the synergistic usage of state-of-the-art passivity-based control and Deep Reinforcement Learning (DRL). The concept of energy tank is used to provide stability guarantees for the interaction controller with uncertain environments, while an online learn- ing policy allows to properly estimate the requirements of the task and adapt the controller accordingly, thus simultaneously achieving stability and performance. The proposed architecture is successfully validated through simulations and experiments with a collaborative manipulator in a surface polishing task.