Online Continual Learning for Control of Mobile Robots

This work presents a novel approach which integrates deep learning, online learning and continual learning paradigms for adaptive control for robotic systems. Deep learning allows generalising knowledge about the robot, while online learning can adapt to variable operating conditions, and continual learning enables remembering previous knowledge. The proposed method approximates the inverse dynamics of the robot, which is formulated as a regression problem. With a minimum knowledge of the robot's dynamics, the proposed method shows its capability to reduce tracking errors online by continuously learning and compensating for internal and external changing conditions. Furthermore, the simulation results show that the proposed approach with online continual learning improves the control performance of ground and aerial mobile robots.